Generalizing diffusion modeling to multimodal, multitask settings

A novel loss function and a way to aggregate multimodal input data are key to dramatic improvements on some test data.

One of the lessons of the machine learning revolution has been that, perhaps counterintuitively, training a model on multiple data types or multiple tasks can improve performance relative to single-purpose models. A model trained on multiple languages, for instance, can learn distinctions that are subtle in one language but pronounced in another, and a model trained on, say, object segmentation may learn properties of visual scenes that help it with depth perception.

Related content
First model to work across a wide range of products uses a second U-Net encoder to capture fine-grained product details.

The advantages of multitask and multimodal training, however, are relatively unexplored in the context of diffusion models, which are responsible for some of the most impressive recent results in generative AI. Diffusion models are trained to incrementally denoise samples to which noise has been incrementally added. The result is that feeding them random noisy inputs will yield randomized outputs that are semantically coherent.

In a paper we presented at the International Conference on Learning Representations (ICLR), we describe a general approach to building multimodal, multitask diffusion models. On the input side, we use modality-specific encoders to map data to a shared diffusion space; on the output side, we use multiple task-specific decoders to map general representations to specific outputs.

MM:MT diffusion architecture.png
The architecture of the multimodal, multitask diffusion model.

The paper presents a theoretical analysis of the problem of generalizing diffusion models to the multimodal, multitask setting, and on the basis of that analysis, it proposes several modifications of the loss function typically used for diffusion modeling. In experiments, we tested our approach on four different multimodal or multitask data sets, and across the board, it was able to match or improve performance relative to single-purpose models.

Minding modality

In the standard diffusion modeling scenario, the model’s encoder maps inputs to a representational space; within that space, a forward process iteratively adds noise to the input representation, and a reverse process iteratively removes it.

Related content
Diffusion modeling within the representational space of a variational autoencoder enables state-of-the-art results.

The loss function includes two terms that measure the distance between the probability distribution of the forward process and the learned probability distribution of the reverse process. One term compares the marginal distributions for the two processes in the forward direction: that is, it compares the likelihoods that any given noisy representation will occur during the forward process. The other term compares the posterior representations of the reverse process — that is, the likelihood that a given representation at time t-1 preceded the representation at time t. We modify these terms so that the distributions are conditioned on the modality of the data — that is, the distributions can differ for data of different modalities.

Both of these loss terms operate in the representational space: they consider the likelihood of a particular representation given another representation. But we also have a term in the loss function that looks at the probability that an input of a given modality led to a particular representation. This helps ensure that the reverse process will correctly recover the modality of the data.

MM:MT diffusion loss.png
The loss function for the multimodal-, multitask diffusion model is the sum of four sublosses, L0–L3. L0 compares the noise distributions of the forward and reverse processes, conditioned on the input data (X). L1 compares posterior distributions, also conditioned on the input data. L2 is the new term in our setting, which induces the model to recover input modalities.

Multimodal means

To fuse the multimodal information used to train the model, we consider the transition distribution in the forward direction, which determines how much noise to add to a given data representation. To compute the mean of that distribution, we define a weighted average of the multimodal input encodings, where the weights are based on input modality.

The transition probability of the forward process. The probability of z sub t, conditioned on z sub t minus 1 and X (the input data) is set equal to a normal distribution whose mean is defined by z sub t minus 1 plus the weighted sum of the encodings of the inputs, sorted by modality. The variance is 1 minus a fraction consisting of a time-varying weight over N (the number of different modalities) plus 1.
The equation for computing the mean and variance of the transition probability of the forward process in the multimodal, multitask setting. N is the number of modalities; wt(i) are the weights assigned to different modalities; xi is the input data; and Ei is the input encoder.

On the basis of the transition probabilities of the forward process, we can now compute the marginal distributions of noisy representations and the posterior distributions of the reverse process (corresponding to sublosses L0 and L1 in the loss function):

The equation for the marginal distribution. The probability of z sub t, conditioned on z sub zero and X (the input data), is set equal to the normal distribution whose mean is the sum of z sub zero (with a coefficient) and a weighted sum of input encoding, sorted by modality. The variance includes a time-varying term (1 minus a time-varying variable), which increases the noise at each time step.
The marginal distribution for the noisy representation zt in the multitask setting (corresponding to subloss L0, above).
The equation for the posterior mean includes a noisy data representation (z sub t), modified by constant factors, from which is subtracted t weighted sum of the encodings of input data of different modalities  (E sub i of x sub i).
The equation for the mean of the posterior distribution, in the multitask setting.

Evaluation

We tested our approach on four tasks, two of which were multitask, and two of which were multimodal. The multitask experiments were both in the vision domain: one involved jointly generating visual data and the associated segmentation masks, and the other was a novel multitask pretraining task in which a diffusion generation model also learned fill in masked regions of input images.

Related content
Generative AI supports the creation, at scale, of complex, realistic driving scenarios that can be directed to specific locations and environments.

The multimodal experiments involved images and other modalities. In one, the model was trained to jointly generate images and their labels, and in the other, the model learned to jointly generate images and their embeddings in a representational space — for instance, CLIP embeddings.

The image segmentation was and embedding generation tasks were chiefly intended as qualitative demonstrations. But the masked pretraining task and the joint generation of images and labels allowed for quantitative evaluation.

Two sets of three images each. In both sets, the first image is of a street scene; the second image is the target segmentation, with objects in the scene masked out in different colors; and the third is the segmentation generated by the model, which is essentially indistinguishable from the target.
Qualitative examples of the segmentation mask generation tasks, with the source image (left), the ground truth segmentation (center), and the masks generated by our method.

We evaluated the masked pretraining model on the task of reconstructing the masked image regions, using learned perceptual image patch similarity (LPIPS) as a metric. LPIPS measures the similarity between two images according to their activations of selected neurons within an image recognition model. Our approach dramatically outperformed the baselines, which were trained only on the reconstruction task, not (simultaneously) on the diffusion task. In some cases, our model’s error rate was almost an order of magnitude lower than the baseline models’.

Two sets of three images each, including a source image, the same image with several black squares of fixed size randomly superimposed upon it, and the model's reconstruction of the complete image.
Our model’s re-creations of masked image regions.

On the task of jointly generating images and labels, our model’s performance was comparable to that of the best baseline vision-language model, with slightly higher precision and slightly lower recall.

For these initial experiments, we evaluated multitask and multimodal performance separately, and each experiment involved only two modalities or tasks. But at least prospectively, the power of our model lies in its generalizability, and in ongoing work, we are evaluating on more than two modalities or tasks at a time and on simultaneous multimodal and multitask training. We are eager to see the result.

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, WA, Seattle
Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads. Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience. As a Prime Video team member, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people. We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you! Key job responsibilities As an Applied Scientist in the Content Understanding Team, you will lead the end-to-end research and deployment of video and multi-modal models applied to a variety of downstream applications. More specifically, you will: - Work backwards from customer problems to research and design scientific approaches for solving them - Work closely with other scientists, engineers and product managers to expand the depth of our product insights with data, create a variety of experiments to determine the high impact projects to include in planning roadmaps - Stay up-to-date with advancements and the latest modeling techniques in the field - Publish your research findings in top conferences and journals About the team Our Prime Video Content Understanding team builds holistic media representations (e.g. descriptions of scenes, semantic embeddings) and apply them to new customer experiences supply chain problems. Our technology spans the entire Prime Video catalogue globally, and we enable instant recaps, skip intro timing, ad placement, search, and content moderation.
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
US, WA, Bellevue
Our mission is to provide consistent, accurate, and relevant delivery information to every single page on every Amazon-owned site. MILLIONS of customers will be impacted by your contributions: The changes we make directly impact the customer experience on every Amazon site. This is a great position for someone who likes to leverage Machine learning technologies to solve the real customer problems, and also wants to see and measure their direct impact on customers. We are a cross-functional team that owns the ENTIRE delivery experience for customers: From the business requirements to the technical systems that allow us to directly affect the on-site experience from a central service, business and technical team members are integrated so everyone is involved through the entire development process. You will have a chance to develop the state-of-art machine learning, including deep learning and reinforcement learning models, to build targeting, recommendation, and optimization services to impact millions of Amazon customers. Do you want to join an innovative team of scientists and engineers who use machine learning and statistical techniques to deliver the best delivery experience on every Amazon-owned site? Are you excited by the prospect of analyzing and modeling terabytes of data on the cloud and create state-of-art algorithms to solve real world problems? Do you like to own end-to-end business problems/metrics and directly impact the profitability of the company? Do you like to innovate and simplify? If yes, then you may be a great fit to join the DEX AI team. Key job responsibilities - Research and implement machine learning techniques to create scalable and effective models in Delivery Experience (DEX) systems - Solve business problems and identify business opportunities to provide the best delivery experience on all Amazon-owned sites. - Design and develop highly innovative machine learning and deep learning models for big data. - Build state-of-art ranking and recommendations models and apply to Amazon search engine. - Analyze and understand large amounts of Amazon’s historical business data to detect patterns, to analyze trends and to identify correlations and causalities - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.