Boosting Self-Supervised Learning via Knowledge Transfer (1805.00385v1)

Abstract: In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset without the need for human annotation. The main objective, however, is to transfer this model to a target domain and task. Currently, the most effective transfer strategy is fine-tuning, which restricts one to use the same model or parts thereof for both pretext and target tasks. In this paper, we present a novel framework for self-supervised learning that overcomes limitations in designing and comparing different tasks, models, and data domains. In particular, our framework decouples the structure of the self-supervised model from the final task-specific fine-tuned model. This allows us to: 1) quantitatively assess previously incompatible models including handcrafted features; 2) show that deeper neural network models can learn better representations from the same pretext task; 3) transfer knowledge learned with a deep model to a shallower one and thus boost its learning. We use this framework to design a novel self-supervised task, which achieves state-of-the-art performance on the common benchmarks in PASCAL VOC 2007, ILSVRC12 and Places by a significant margin. Our learned features shrink the mAP gap between models trained via self-supervised learning and supervised learning from 5.9% to 2.6% in object detection on PASCAL VOC 2007.

• The paper introduces a novel method that decouples the self-supervised pretext architecture from the target fine-tuning model.
• It employs clustering-based pseudo-labeling to distill learned representations, enhancing transferability across varied network architectures.
• Experimental results show a significant reduction in the performance gap between self-supervised and supervised learning on major benchmarks.

Knowledge Transfer in Self-Supervised Learning: A Detailed Exploration

The paper put forth by Noroozi et al. offers an intricate framework for improving self-supervised learning (SSL) through knowledge transfer methods. The central proposition of this paper involves the decoupling of the self-supervised model architecture from that of the target task's fine-tuned model, which allows for a more nuanced examination and comparison of various models and pretext tasks.

Key Contributions

The authors introduce a knowledge transfer method that leverages clustering to distill learned representations into pseudo-labels. This method does not constrain itself to softmax-based output or fixed dimensionality, thus providing a versatile mechanism to transfer knowledge across different network architectures.

• Decoupling Models: By allowing different architectures for the pretext task and final fine-tuning, the method unlocks the possibility of transferring knowledge from deeper, more complex models to shallower models designed for the target task.
• Clustering-Based Pseudo-Labeling: The features learned through SSL are clustered, and these cluster identities serve as pseudo-labels for a new, smaller model. This technique is stated to better capture the structural knowledge embedded within the model compared to the traditional distillation methods.

Practical and Theoretical Implications

The author's experimental validation on notable benchmarks such as PASCAL VOC 2007, ILSVRC12, and Places demonstrates enhanced performance metrics when compared to previous works. Notably, the gap between self-supervised and supervised learning diminishes significantly from 5.9% to 2.6% in object detection tasks. This framework sets a precedent for future works that might incorporate deeper models or large-scale datasets into the pretext training phases without sacrificing compatibility with the target models.

Future Developments and Speculative Outcomes

The flexibility introduced by the knowledge transfer method could potentially allow for more intricate architectures suitable for the SSL phase, such as ResNet or deeper variants. As datasets grow in size, employing such architectures could lead to even better pretext learned features, thereby offering improved generalization in final task models. It is reasonable to posit that further evolution of such methods will likely continue to refine the understanding of clustering-based representation transfer and its scalability across varied data domains.

In summary, this work delineates a robust approach toward knowledge transfer in SSL, facilitating richer representations and improved outcomes on standard vision benchmarks. Through its innovative clustering approach, it catalyzes further exploration into SSL techniques devoid of architecture compatibility constraints.