In-Context Symmetries: Self-Supervised Learning through Contextual World Models (2405.18193v1)

Abstract: At the core of self-supervised learning for vision is the idea of learning invariant or equivariant representations with respect to a set of data transformations. This approach, however, introduces strong inductive biases, which can render the representations fragile in downstream tasks that do not conform to these symmetries. In this work, drawing insights from world models, we propose to instead learn a general representation that can adapt to be invariant or equivariant to different transformations by paying attention to context -- a memory module that tracks task-specific states, actions, and future states. Here, the action is the transformation, while the current and future states respectively represent the input's representation before and after the transformation. Our proposed algorithm, Contextual Self-Supervised Learning (ContextSSL), learns equivariance to all transformations (as opposed to invariance). In this way, the model can learn to encode all relevant features as general representations while having the versatility to tail down to task-wise symmetries when given a few examples as the context. Empirically, we demonstrate significant performance gains over existing methods on equivariance-related tasks, supported by both qualitative and quantitative evaluations.

• The paper introduces ContextSSL, a method that adapts vision representations by learning task-specific invariance and equivariance through contextual cues.
• It employs a transformer-based contextual module inspired by world models to align representations without rigid, predefined augmentations.
• Empirical evaluations on CIFAR-10 and 3DIEBench demonstrate enhanced accuracy and retrieval performance, validating its adaptive learning approach.

Contextual Self-Supervised Learning (ContextSSL) for Adaptive Vision Representations

The paper "Contextual Self-Supervised Learning (ContextSSL)" addresses a fundamental limitation in the domain of self-supervised learning (SSL) for vision: the reliance on predefined symmetries to enforce invariance or equivariance to data augmentations. Standard SSL methodologies, including those based on joint-embedding architectures, often incorporate specific inductive priors through data transformations. These transformations, such as color jitter or rotations, help create positive pairs in contrastive learning, which can significantly improve the learning process. However, they also introduce a source of rigidity, making the learned representations fragile when applied to downstream tasks that do not share the same symmetries. This paper proposes an innovative method, ContextSSL, which adapts to task-specific symmetries by leveraging context.

ContextSSL: An Overview

Key Idea

The core idea behind ContextSSL is to introduce a context-aware memory module that tracks task-specific states, actions, and future states. In this framework, actions are transformations applied to the input data, and the states represent the input's representation before and after the transformation. Unlike existing approaches, ContextSSL emphasizes learning a general representation that can adaptively become invariant or equivariant depending on the context. This approach alleviates the need for predefined augmentation strategies and allows the model to dynamically adjust based on the requirements of the downstream task.

World Models Inspiration

ContextSSL draws inspiration from world models commonly employed in reinforcement learning (RL). In these models, the agent learns representations of the environment through past experiences, predicting future states based on current states and actions. The adaptation of world modeling to vision representation is a relatively new concept, introduced through Image World Models (IWM). IWMs treat transformations as actions and their effects on representations as state changes over time. However, previous IWM frameworks also impose fixed equivariance to predefined actions, a limitation that ContextSSL overcomes by incorporating a contextual module.

Technical Contributions

ContextSSL Framework

ContextSSL enhances the joint-embedding architecture with a contextual transformer module, which adapts to selective invariance or equivariance based on a finite context of demonstrations representing task-specific symmetries. This approach consists of encoding the input pairs in the context, leveraging the selective inclusion of transformation parameters to enforce equivariance, and excluding them to enforce invariance. The architecture achieves this alignment without undergoing any parameter updates, facilitated by a carefully designed masking strategy to prevent shortcut learning.

1. Symmetries as Context: Transformation groups are used to construct the context that informs the model of the symmetries relevant to the task.
2. Contextual World Models: The transformer-based module pays attention to the context, facilitating the alignment of representations.
3. Loss Function: The model employs an InfoNCE loss, adapted to minimize at each context length along with an auxiliary predictor to prevent collapse to trivial solutions of invariance.

Empirical Evaluation

Benchmarks and Datasets

The efficacy of ContextSSL is empirically validated on the 3D Invariant Equivariant Benchmark (3DIEBench) and CIFAR-10, focusing on tasks involving rotations, cropping, blurring, and color transformations. The paper compares ContextSSL against a range of invariant and equivariant baseline methods, such as SimCLR, VICReg, EquiMOD, SEN, and SIE. Performance metrics include downstream classification accuracy and $R^2$ scores for augmentation prediction tasks.

Quantitative Results

ContextSSL demonstrates superior performance in learning adaptive symmetries:

• Achieves high $R^2$ scores in rotation and color prediction tasks compared to baseline methods.
• Shows robust downstream classification accuracy, comparable to invariant learning methods like SimCLR.
• Exhibits consistent improvement in nearest neighbor retrieval tasks (MRR and H@k) with increasing context length.

Qualitative Results

The qualitative assessment through nearest neighbor retrieval tasks reveals that ContextSSL accurately identifies the target rotation angle while remaining invariant to irrelevant attributes like color, which baseline models struggle to achieve consistently.

Practical Implications and Future Work

Broader Implications

The ability of ContextSSL to dynamically adapt to task-specific symmetries without retraining the model for each augmentation type presents significant implications for both practical deployments and theoretical advancements in vision representation learning. It provides a pathway towards developing more versatile and robust vision models capable of handling a diverse array of tasks in dynamic environments.

Future Directions

Future research can expand on naturally occurring symmetries beyond hand-crafted transformations. Additionally, further exploration of context creation and transformer-based architectures could enhance the adaptability and performance of SSL methods. The intersection of SSL and world models also opens new avenues for exploring how these paradigms can be integrated for more generalized AI systems.

Conclusion

Contextual Self-Supervised Learning (ContextSSL) introduces a transformative approach to self-supervised vision representation by leveraging context to dynamically adapt to task-specific symmetries. This methodology effectively addresses the rigidities of existing SSL frameworks, paving the way for more flexible and adaptive vision models. ContextSSL holds the promise of bridging the gap between the current state of SSL and the adaptive, context-aware learning exhibited by humans.