Joint Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self Supervised Learning (2505.12477v1)

Abstract: Reconstruction and joint embedding have emerged as two leading paradigms in Self Supervised Learning (SSL). Reconstruction methods focus on recovering the original sample from a different view in input space. On the other hand, joint embedding methods align the representations of different views in latent space. Both approaches offer compelling advantages, yet practitioners lack clear guidelines for choosing between them. In this work, we unveil the core mechanisms that distinguish each paradigm. By leveraging closed form solutions for both approaches, we precisely characterize how the view generation process, e.g. data augmentation, impacts the learned representations. We then demonstrate that, unlike supervised learning, both SSL paradigms require a minimal alignment between augmentations and irrelevant features to achieve asymptotic optimality with increasing sample size. Our findings indicate that in scenarios where these irrelevant features have a large magnitude, joint embedding methods are preferable because they impose a strictly weaker alignment condition compared to reconstruction based methods. These results not only clarify the trade offs between the two paradigms but also substantiate the empirical success of joint embedding approaches on real world challenging datasets.

Provable Benefits of Latent Space Prediction for Self-Supervised Learning

The paper entitled "Joint-Embedding vs Reconstruction: Provable Benefits of Latent Space Prediction for Self-Supervised Learning" explores the performance trade-offs between two prevalent approaches in Self-Supervised Learning (SSL): reconstruction and joint-embedding methods. Through rigorous theoretical analysis, the authors provide closed-form solutions that shed light on the impact of data augmentation on learned representations. Furthermore, the paper offers guidance on the selection of these methods based on the characteristics of irrelevant features in datasets.

This paper begins by establishing a context where SSL does not require prior knowledge of data's informative attributes but assumes knowledge of uninformative features that should be disregarded. Two predominant methods are explored in learning data representations: reconstruction-based and joint-embedding approaches. Whereas reconstruction-based methods strive to recover original signals post-augmentation, joint-embedding methods generate similar latent representations for augmented samples while ensuring dissimilarity to representations of different samples.

The paper provides closed-form solutions for linear models using reconstruction-based (\Cref{prop:closed_form_reconstruction}) and joint-embedding (\Cref{prop:closed_form_ssl}) SSL methods. These solutions explicitly characterize the influence of data augmentation, which serves as a foundation to understand the trade-offs between both paradigms. An exquisite detail of their analysis is the recognition that SSL models require a sufficient alignment between augmentations and irrelevant features to reach asymptotic optimality, which contrasts markedly with supervised learning. Supervised learning models demonstrate the ability to achieve optimal performance either with augmented noise alignment or by leveraging large sample sizes, as described in \Cref{prop:ols_asymptotic}.

A critical takeaway from this work is a nuanced understanding of the alignment requirements between augmentations and noise for SSL methods, derived from \Cref{theorem:impact_DA_reconstruction} and \Cref{theorem:impact_DA}. Specifically, the paper highlights that joint-embedding methods often prevail against reconstruction-based methods when irrelevancies within datasets have large magnitudes (\Cref{cor:comparison_je_vs_reconstruction}).

Experiments on linear models validate that joint-embedding methods are preferable under high noise scenarios due to better robustness (\Cref{sec:exp_linear_models_main}). This is corroborated with experiments using deep networks on corrupted image datasets like ImageNet-C and CIFAR-10 in \Cref{sec:exp_deep_networks}, where the robustness of joint-embedding methods such as DINO and BYOL compared to MAE was distinctly demonstrated (\Cref{tab:results_robustness_ssl_main_with_drop}).

Furthermore, ablation studies emphasize the salient role of aligning augmentations with underlying noise for enhancing SSL performance, particularly under data corruption scenarios (\Cref{sec:analysis_nois_injection}). These insights make substantial contributions to the theoretical understanding of SSL methods and provide practical guidelines for implementation choices.

In conclusion, the paper offers a significant contribution toward the theoretical and empirical understanding of SSL, detailing conditions under which each method should be favored. This work has strong practical implications by underscoring that the choice between joint-embedding and reconstruction-based methods can effectively be driven by noise characteristics and available augmentations within real-world data applications. Future work could focus on extending these theoretical findings to scenarios involving finite sample sizes to further elucidate the interplay between sample complexity and data augmentation in SSL settings.