High Fidelity Visualization of What Your Self-Supervised Representation Knows About (2112.09164v2)

Abstract: Discovering what is learned by neural networks remains a challenge. In self-supervised learning, classification is the most common task used to evaluate how good a representation is. However, relying only on such downstream task can limit our understanding of what information is retained in the representation of a given input. In this work, we showcase the use of a Representation Conditional Diffusion Model (RCDM) to visualize in data space the representations learned by self-supervised models. The use of RCDM is motivated by its ability to generate high-quality samples -- on par with state-of-the-art generative models -- while ensuring that the representations of those samples are faithful i.e. close to the one used for conditioning. By using RCDM to analyze self-supervised models, we are able to clearly show visually that i) SSL (backbone) representation are not invariant to the data augmentations they were trained with -- thus debunking an often restated but mistaken belief; ii) SSL post-projector embeddings appear indeed invariant to these data augmentation, along with many other data symmetries; iii) SSL representations appear more robust to small adversarial perturbation of their inputs than representations trained in a supervised manner; and iv) that SSL-trained representations exhibit an inherent structure that can be explored thanks to RCDM visualization and enables image manipulation.

• The paper demonstrates that SSL backbone representations are sensitive to data augmentations, in contrast to invariant post-projector embeddings.
• The paper shows that self-supervised representations exhibit enhanced robustness against adversarial perturbations compared to supervised counterparts.
• The paper employs Representation Conditional Diffusion Models (RCDM) to uncover and visualize structural features for applications like image editing.

High Fidelity Visualization of Self-Supervised Representations

The paper "High Fidelity Visualization of What Your Self-Supervised Representation Knows About" by Bordes et al. explores the understanding of neural representations via self-supervised learning (SSL), using a novel visualization technique to analyze what these representations capture. This approach is crucial because it moves beyond the traditional method that solely relies on downstream classification tasks to evaluate SSL models, thereby providing a richer understanding of the learned representations.

Overview

Neural networks, particularly those trained using SSL, have demonstrated proficiency in learning robust representations from unlabeled data by engaging with pretext tasks. These tasks, such as context prediction or transformation recognition, have been successful across domains, particularly in NLP and computer vision. However, there exists a gap in understanding the extent of what these neural representations capture beyond classification utility. Bordes et al. address this gap by employing Representation Conditional Diffusion Models (RCDM) which allows for high-fidelity visualization of SSL representations directly in data space.

Key Findings

1. Invariance to Data Augmentations: The paper debunks the common belief that SSL backbone representations are invariant to the data augmentations they were trained with. It was found that SSL backbone representations are indeed sensitive to these data augmentations whereas the post-projector embeddings are invariant. This is an important insight because it indicates that augmentations influence the robustness of learned features.
2. Robustness to Adversarial Perturbations: SSL representations demonstrated greater robustness to small adversarial perturbations compared to supervised representations. This implies an inherent stability in SSL models that may be capitalized upon for building more resilient models.
3. Structured Representation Exploration: By using RCDMs, the research discovered inherent structural features within SSL representations, allowing manipulation of the learned representations for image editing, such as background substitution.

Methodology

The paper utilizes RCDM, which are adept at generating high-quality samples that closely mimic state-of-the-art generative models while ensuring that the conditioned samples reflect the intended representations. The approach involved training diffusion models conditioned on SSL representations, thereby providing a pathway to backtrack from representation to the original data space.

Implications

The theoretical implications of this paper emphasize the necessity to reconsider the evaluation criteria for SSL methods, advocating for more holistic approaches beyond classification metrics. In practical terms, the visualization tools introduced, like RCDM, can revolutionize how researchers and practitioners explore and optimize SSL models, potentially leading to more refined and nuanced models.

Speculation on Future Developments

The ability to qualitatively analyze neural representations opens numerous avenues for future research. This capability could lead to developing methodologies that tailor SSL models to specific tasks, optimizing them for finer details beyond simple accuracy metrics. Moreover, understanding the robust properties of SSL models may contribute to advancements in adversarial defense strategies within AI systems.

In conclusion, the paper by Bordes et al. provides significant insights into the capabilities of self-supervised learning representations, challenging the preexisting boundaries on how neural network efficacy is measured and understood. This research is pivotal as it fosters a more comprehensive exploration of neural architectures, advancing the domain of AI and machine learning.