New contrastive-learning methods for better data representation

New loss functions enable better approximation of the optimal loss and more-useful representations of multimodal data.

Many recent advances in artificial intelligence are the result of representation learning: a machine learning model learns to represent data items as vectors in a multidimensional space, where geometric relationships between vectors correspond to semantic relationships between items.

The M5 team at Amazon strives to construct general-purpose semantic representations of data related to the Amazon Store — product descriptions, queries, reviews, and more — that can be employed by machine learning (ML) systems throughout Amazon. Our approach involves leveraging all accessible data for each entity, often spanning multiple modalities.

One of the most successful ways to produce general-purpose representations is through contrastive learning, in which a model is trained on pairs of inputs, which are either positive (similar inputs/products) or negative (dissimilar inputs/products). The model learns to pull positive examples together and push negative examples apart.

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In a pair of recent papers, M5 researchers have made substantial contributions to the theory and practice of contrastive learning. In “Why do we need large batch sizes in contrastive learning? A gradient-bias perspective”, presented at the 2022 Neural Information Processing Systems (NeurIPS) conference, we propose a new contrastive-learning loss function that enables models to converge on useful representations with lower memory cost and less training data.

And in “Understanding and constructing latent modality structures in multi-modal representation learning”, presented at this year’s Computer Vision and Pattern Recognition conference (CVPR), we propose geometric constraints on the representations of different modes of the same data item — say, image and text — that are more useful for downstream tasks than simply trying to resolve both representations to the same point in the representational space.

Do we need large batch sizes in contrastive learning?

In contrast with standard ML methods, contrastive learning typically requires very large batch sizes to achieve good performance: several popular models, for instance, require tens of thousands of training examples, significantly increasing the memory overhead; reducing the batch size can impair performance. In our NeurIPS paper, we attempt to understand this phenomenon and to propose techniques for mitigating it.

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Part of the appeal of contrastive learning is that it’s unsupervised, meaning it doesn’t require data annotation. Positive pairs can be generated by mathematically transforming an “anchor sample” and pairing the transformed version with the original; negative pairs can be generated by pairing an anchor sample with transformed versions of other anchor samples. With image data, a transformation might involve re-cropping, reversing, or distorting the colors of the anchor sample; with textual data, a transformation might involve substituting synonyms for the words in a sentence.

Given a measure of similarity between vectors in the representational space, the standard loss function for contrastive learning involves a ratio whose numerator includes the similarity between an anchor sample and one of its transformations; the denominator includes the sum of the similarities of the anchor sample and all possible negative samples. The goal of training is to maximize that ratio.

In principle, given the possibility of applying transformations to negative samples, “all possible negative samples” could describe an infinite set. In practice, contrastive learning typically just relies on the negative examples available in the training batch. Hence the need for large batch sizes — to approximate an infinite sum.

contrastive_learning [Read-Only].png
The contrastive-learning framework. Approximating an infinite sum with the samples in a finite minibatch of training data can introduce gradient bias.

If the distribution of minibatch samples differs from the distribution of possible negatives, however, this approximation can bias the model. One difficulty in correcting the bias is that, because the loss function contrasts each positive pair with all possible negatives at once, in a ratio, it cannot be decomposed into a sum of sub-losses.

We address the decomposability problem using Bayesian augmentation. The general approach is that, for each anchor sample, we create a random auxiliary variable, which can be thought of as a weight applied to the anchor sample’s similarity scores. Using identity under the gamma function, we can show that the auxiliary variable follows a gamma distribution, which is easy to sample. As a consequence, we can rewrite the loss in an exponential rather than a fractional form, making it decomposable.

During training, we begin by sampling the auxiliary variables for the current batch of data from a gamma distribution, giving us the weight of the similarity scores for all the anchor samples. Conditioned on the sampled values, we then apply maximum likelihood estimation to optimize the parameters of the model, which will consider the sampled weights on the similarity scores from the first step. We then repeat this process for the entire dataset, summing a sequence of (weighted) sub-losses to produce a cumulative loss. In our paper, we show that this procedure will converge toward the expected loss for the original contrastive-loss function, with its infinite sum in the denominator.

Contrastive-learning losses.png
Results of 10 training runs on synthetic data with added noise, comparing a model trained with our decomposable loss function (red) to one trained with the conventional loss function (blue). With our loss, the model consistently converged to the optimum (1.0), while with the conventional loss, it never did.

We evaluate our approach through a number of experiments. In one, we used simulated data, into which we injected noise to simulate bias. Then we used both our loss and the conventional loss function to train a model 10 times, with different initialization values. At heavy noise levels, the model trained with the conventional loss failed to converge, while ours consistently converged to the optimum.

We also evaluated the models on a variety of downstream tasks, including zero-/few-shot image classification and image/text retrieval. Our approach showed significant performance improvement over state-of-the-art baseline methods.

What geometries work best for multimodal representation matching?

At M5, we are building scalable models that can handle multimodal data — for instance, multilingual models that translate between product descriptions in different languages or multi-entity models that jointly model different images of the same product. Contrastive learning is a promising method for building such models: data in different modalities that are associated with the same products can be treated as positive pairs, and contrastive learning pulls them together in the representational space.

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We theoretically investigated whether the standard contrastive-learning framework is optimal in terms of the prediction error rate on downstream tasks, and the surprising answer is no. In our CVPR paper, we prove that if the information gap between two modalities is large — that is, if you can’t infer much about one modality from the other — then the best prediction error we can hope to achieve using standard contrastive-learning representations is larger than that we can achieve if we simply train a machine learning model directly on data in a single modality.

This makes some intuitive sense. Ideally, contrastive learning would pull the different modalities so tightly together that they would essentially resolve to a single point in the representational space. But of course, the reason to use multimodal representations for downstream tasks is that each modality may capture useful information that the other does not. Collapsing the different modalities’ representations together neutralizes this advantage.

Consequently, in our CVPR paper, we explore different geometrical relationships in the representational space that can establish correlations between multimodal data without sacrificing information specific to each mode. We propose three general approaches to constructing modality structures in the representational space, suited to intramodal representation, intermodal representation, and a combination of the two:

  1. a deep feature separation loss for intramodality regularization, which uses two types of neural network components to separate different modality information: one component captures information that’s shared between modalities (tuned according to the standard contrastive-learning loss), and the other, which is orthogonal to the first, captures information unique to the modality;
  2. a “Brownian-bridge” loss for intermodality regularization, which uses Brownian motion to plot several trajectories/transitions between the representation of one modality (say, text) and the other (say, an image) and constrains representations of augmented data to lie along one of those paths; and
  3. a geometric-consistency loss for both intra- and intermodality regularization, which enforces symmetry in the geometric relationships between representations in one modality and the corresponding representations in the other modality, while simultaneously enforcing symmetries in cross-modal geometric relationships.
Contrastive learning.png
Three types of modality structures that can improve modality representation learning for downstream tasks. (1) With deep feature separation, a model produces two orthogonal vectors for each modality, one that encodes information shared across modalities and one that encodes mode-specific information. (2) Brownian bridges use Brownian motion to generate trajectories/transitions between representations of data in different modes, defining a subspace in which the representations of augmented data are induced to lie. (3) Geometric consistency enforces symmetries in the relationships between data representations, both within modes (orange-orange and blue-blue) and across modes (blue-orange).

We have conducted extensive experiments on two popular multimodal representation-learning frameworks, the CLIP-based two-tower model and the ALBEF-based fusion model. We tested our model on a variety of tasks, including zero-/few-shot image classification, image-text retrieval, visual question answering, visual reasoning, and visual entailment. Our method achieves consistent improvements over existing methods, demonstrating the effectiveness and generalizability of our proposed approach on multimodal representation learning.

Going forward

Our NeurIPS and CVPR papers represent only two interesting projects from our M5 team. There is a lot more research on multimodal learning going on in M5. This includes generative models for images, videos, and text (e.g. Stable Diffusion, DreamBooth) to enable data synthesis and representation learning and training and applying large language models to enhance customer shopping experiences. We expect to report on more research highlights in the near future.

Research areas

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Can successfully sell ideas to an executive level decision maker. - Mentors and trains the research scientist community on complex technical issues. AWS has the most services and more features within those services, than any other cloud provider–from infrastructure technologies like compute, storage, and databases–to emerging technologies, such as machine learning and artificial intelligence, data lakes and analytics, and Internet of Things. Whether its Identity features such as access management and sign on, cryptography, console, builder & developer tools, and even projects like automating all of our contractual billing systems, AWS Platform is always innovating with the customer in mind. The AWS Platform team sustains over 750 million transactions per second. We have a formal mentor search application that lets you find a mentor that works best for you based on location, job family, job level etc. Your manager can also help you find a mentor or two, because two is better than one. In addition to formal mentors, we work and train together so that we are always learning from one another, and we celebrate and support the career progression of our team members. Key job responsibilities Technical Responsibilities: - Interact with various teams to develop an understanding of their security and safety requirements. - Apply the acquired knowledge to build tools find problems, or show the absence of security/safety problems. - Implement these tools through the use of SAT, SMT, BDDs, and various concepts from programming languages, theorem proving, formal verification and constraint solving. - Perform analysis of the customer systems using tools developed in-house or externally provided - Create software prototypes to verify and validate the devised solutions methodologies; integrate the prototypes into production systems using standard software development tools and methodologies. Leadership Responsibilities: - Can present and defend company-wide technical decisions to the internal technical community and represent the company effectively at technical conferences. - Functional thought leader, sought after for key tech decisions. Can successfully sell ideas to an executive level decision maker. - Mentors and trains the research scientist community on complex technical issues. A day in the life You will be working on cutting edge technology related to formal methods, automated reasoning, automated testing, and adjacent areas. You will work with fellow applied scientists to solve challenging problems that provide value to customers by improving the quality of software. You will have an opportunity to publish your work. Why AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Mentorship and Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. About the team The Automated Reasoning in Identity (ARI) team is growing fast. It works on applying automated reasoning techniques to services within AWS's Identity organization, building on initial successes of the Zelkova and Access Analyzer projects. The reach of AR within Identity is growing, with more scientists joining all the time.