Occam's Razor for Self Supervised Learning: What is Sufficient to Learn Good Representations? (2406.10743v1)

Abstract: Deep Learning is often depicted as a trio of data-architecture-loss. Yet, recent Self Supervised Learning (SSL) solutions have introduced numerous additional design choices, e.g., a projector network, positive views, or teacher-student networks. These additions pose two challenges. First, they limit the impact of theoretical studies that often fail to incorporate all those intertwined designs. Second, they slow-down the deployment of SSL methods to new domains as numerous hyper-parameters need to be carefully tuned. In this study, we bring forward the surprising observation that--at least for pretraining datasets of up to a few hundred thousands samples--the additional designs introduced by SSL do not contribute to the quality of the learned representations. That finding not only provides legitimacy to existing theoretical studies, but also simplifies the practitioner's path to SSL deployment in numerous small and medium scale settings. Our finding answers a long-lasting question: the often-experienced sensitivity to training settings and hyper-parameters encountered in SSL come from their design, rather than the absence of supervised guidance.

• The paper demonstrates that DIET simplifies SSL by transforming unsupervised learning into a supervised approach that achieves competitive results.
• The methodology employs a linear classifier with a cross-entropy loss, eliminating the need for complex architectures and extensive hyper-parameter tuning.
• Empirical evaluations across natural, medical, and small datasets confirm DIET's robustness, stability, and broad applicability.

Occam's Razor for Self-Supervised Learning: What is Sufficient to Learn Good Representations?

Introduction

The paper "Occam's Razor for Self-Supervised Learning: What is Sufficient to Learn Good Representations?" by Mark Ibrahim, David Klindt, and Randall Balestriero critically evaluates current practices in Self-Supervised Learning (SSL), specifically focusing on the efficacy of various intricate designs that have been introduced to enhance the quality of learned representations. The authors argue that these additional mechanisms, while beneficial for certain large-scale datasets, may not be essential for small to medium datasets. They propose a simpler alternative, referred to as DIET (Data-Independent Embedding Training), and empirically demonstrate that this minimalistic approach can achieve competitive performance while offering greater stability and reduced need for hyper-parameter tuning.

Methodology

The authors start by deconstructing the modern SSL pipelines, which typically involve components such as projector networks, positive views, and teacher-student architectures. They then theorize that many of these components are superfluous for datasets of a certain size. The primary innovation proposed is the DIET objective, which simplifies SSL paradigms by treating each sample in the dataset as its own class. This approach effectively transforms unsupervised learning into a supervised problem without the complex machinery traditionally required.

The methodology primarily revolves around an absolute loss function based on cross-entropy, without the need for nonlinear projectors or the complex generation and management of positive pairs. The approach also excludes the moving average teacher models often employed to prevent representation collapse. DIET's architecture consists merely of a linear classifier appended to the output of a deep neural network (DNN), forming a straightforward yet effective pipeline.

Empirical Evaluation

Performance on Natural Images

The experiment section begins with an evaluation on CIFAR100, where DIET is compared against several SSL benchmarks across different architectures. The results show that DIET is capable of achieving and sometimes surpassing the performance of state-of-the-art SSL methods. This observation extends to other medium-scale datasets such as TinyImagenet and Imagenet100. Intriguingly, DIET consistently maintains high performance across varying architectures, including Resnet variants, Vision Transformers, and ConvNexts, among others.

A particularly striking aspect of DIET is its robustness to different data modalities. The authors extend their experiments to smaller datasets such as Food101 and CUB-200, where they demonstrate that DIET can compete with or even outperform models pre-trained on larger datasets through transfer learning.

Medical Images

The generalization of DIET's efficacy to medical images is particularly noteworthy. The authors experimented with the MedMNISTv2 benchmark datasets (PathMNIST, DermaMNIST, and BloodMNIST). Unlike traditional SSL methods, which struggle without extensive hyper-parameter tuning, DIET shows superior performance out-of-the-box. This highlights DIET’s potential in domains where data is both limited and far removed from the types typically encountered in natural image datasets.

Ablation Studies

Extensive ablation studies validate DIET’s stability and robustness. The authors explore the impact of various factors such as data augmentation strength, training epochs, and batch size. They find that DIET’s performance does not degrade appreciably with smaller batch sizes, making it suitable for single-GPU training. Moreover, the training loss of DIET is informative of its downstream performance, which is rarely the case for most SSL methods.

Theoretical Insights

The paper does not shy away from theoretical substantiation. Through a simplified linear model analysis, the authors demonstrate that DIET performs a form of low-rank approximation of the input data matrix. This insight provides a theoretical underpinning for its empirical success and opens the prospect for more rigorous theoretical studies in the future.

Implications and Future Directions

The implications of this paper are twofold. Practically, DIET's simplicity reduces the barriers for deploying SSL across a broader range of applications, including domains with limited computational resources and diverse data modalities. Theoretically, DIET’s stripped-down nature makes it amenable to formal analysis, thereby paving the way for novel and provable SSL solutions.

Future research could focus on scaling DIET to larger datasets, possibly through more sophisticated sub-sampling strategies or adaptive learning mechanisms. Alongside, understanding the interactions between DIET and various neural architectures could provide additional insights into optimizing SSL pipelines.

Conclusion

The paper presents a compelling argument for re-evaluating the complexity of current SSL pipelines. Through DIET, the authors illustrate that many of the intricate designs traditionally considered indispensable may, in fact, be superfluous for small to medium-scale datasets. The proposed methodology not only delivers competitive performance but also introduces a new level of stability and simplicity, making it an attractive alternative for both practical applications and theoretical exploration.