Reverse Engineering Self-Supervised Learning

In the latest episode of the Talking Papers Podcast, I had the pleasure of hosting Ravid Shwartz-Ziv, a brilliant early career academic, to discuss his recent research paper titled “Reverse Engineering Self-Supervised Learning,” which was published at NeurIPS 2023. Coming from a background in machine learning, I was particularly excited about this paper as it delves into understanding the mechanisms and representations learned through self-supervised learning (SSL).

The paper presents an extensive empirical analysis of SSL-trained representations, exploring different models, architectures, and hyperparameters. One of the intriguing findings is that SSL inherently facilitates the clustering of samples based on semantic labels, driven by the regularization term of the SSL objective. This clustering process not only enhances downstream classification but also showcases the compression power of SSL-trained representations. Additionally, the study establishes that these representations align more closely with semantic classes as compared to random classes, across various hierarchical levels. Importantly, this alignment increases during training and as the network goes deeper.

What makes this paper unique is that it focuses on understanding the semantic clustering effect of SSL methods rather than solely showcasing superior performance on benchmark datasets. This deeper exploration provides valuable insights into the representation learning mechanisms of SSL and their impact on performance with different sets of classes. Furthermore, it highlights the potential for compression in SSL representations, which can have significant implications in practical applications.

During our conversation, Ravid and I shared a connection as colleagues in the field, both based in Israel. Interestingly, despite our proximity, we had never met in person. This paper falls into a genre that I personally find fascinating, as it seeks to comprehend the underlying capabilities of the tools we commonly employ. Ravid’s work and dedication as a CDS Faculty Fellow at NYU Center for Data Science are evident in his research, and I am truly excited to see what future insights his endeavors will bring.

To stay updated on our latest podcast episodes and discussions on cutting-edge research papers like this, make sure to tune in to the Talking Papers Podcast. Join the conversation by using the hashtag #TalkingPapersPodcast.

AUTHORS


Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel, Yann LeCun

ABSTRACT


Self-supervised learning (SSL) is a powerful tool in machine learning, but understanding the learned representations and their underlying mechanisms remains a challenge. This paper presents an in-depth empirical analysis of SSL-trained representations, encompassing diverse models, architectures, and hyperparameters. Our study reveals an intriguing aspect of the SSL training process it inherently facilitates the clustering of samples with respect to semantic labels, which is surprisingly driven by the SSL objective’s regularization term. This clustering process not only enhances downstream classification but also compresses the data information. Furthermore, we establish that SSL-trained representations align more closely with semantic classes rather than random classes. Remarkably, we show that learned representations align with semantic classes across various hierarchical levels, and this alignment increases during training and when moving deeper into the network. Our findings provide valuable insights into SSL’s representation learning mechanisms and their impact on performance across different sets of classes.

RELATED WORKS

๐Ÿ“šSimCLR

๐Ÿ“šVICReg

๐Ÿ“šPrevalence of neural collapse

LINKS AND RESOURCES

๐Ÿ“šPreprint

To stay up to date with his latest research, follow on:

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๐ŸฆTwitter

๐Ÿ‘จ๐Ÿปโ€๐ŸŽ“LinkedIn

This episode was recorded on October 30th 2023

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