$\mathbb{X}$-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs (2407.18134v2)

Abstract: Learning good representations involves capturing the diverse ways in which data samples relate. Contrastive loss - an objective matching related samples - underlies methods from self-supervised to multimodal learning. Contrastive losses, however, can be viewed more broadly as modifying a similarity graph to indicate how samples should relate in the embedding space. This view reveals a shortcoming in contrastive learning: the similarity graph is binary, as only one sample is the related positive sample. Crucially, similarities \textit{across} samples are ignored. Based on this observation, we revise the standard contrastive loss to explicitly encode how a sample relates to others. We experiment with this new objective, called $\mathbb{X}$-Sample Contrastive, to train vision models based on similarities in class or text caption descriptions. Our study spans three scales: ImageNet-1k with 1 million, CC3M with 3 million, and CC12M with 12 million samples. The representations learned via our objective outperform both contrastive self-supervised and vision-LLMs trained on the same data across a range of tasks. When training on CC12M, we outperform CLIP by $0.6\%$ on both ImageNet and ImageNet Real. Our objective appears to work particularly well in lower-data regimes, with gains over CLIP of $16.8\%$ on ImageNet and $18.1\%$ on ImageNet Real when training with CC3M. Finally, our objective seems to encourage the model to learn representations that separate objects from their attributes and backgrounds, with gains of $3.3$-$5.6$\% over CLIP on ImageNet9. We hope the proposed solution takes a small step towards developing richer learning objectives for understanding sample relations in foundation models.

$\mathbb{X}$-Sample Contrastive Loss: Enhancing Representation Learning Through Sample Similarity Graphs

Overview

The paper "$\mathbb{X}$-Sample Contrastive Loss: Improving Contrastive Learning with Sample Similarity Graphs" introduces an innovative approach to contrastive loss aiming to improve representation learning by explicitly incorporating the similarities across samples within a dataset. The authors propose the $\mathbb{X}$-Sample Contrastive Loss ($\mathbb{X}$-CLR), a method designed to construct a richer similarity graph with continuous values rather than the binary designation typical of traditional contrastive learning approaches.

Problem and Motivation

Contrastive loss has been influential in methods ranging from self-supervised learning (SSL) to multimodal learning. Standard contrastive objectives treat sample relationships in a binary manner—each sample is either a positive or a negative pair. This binary treatment fails to capture nuanced inter-sample relationships, potentially overlooking valuable contextual information that could enhance the quality of learned representations.

Methodology

The key contribution of this paper is modifying the traditional contrastive loss to employ a similarity graph with continuous scalars. Here are the primary innovations:

1. Similarity Graph Construction: Instead of binary relationships, the proposed $\mathbb{X}$-CLR utilizes a similarity graph where continuous scalars indicate the extent to which two samples are related.
2. Training Objective: The paper revises the standard InfoNCE objective to incorporate these soft similarities, leading to the formulation of the $\mathbb{X}$-CLR loss. This method allows the incorporation of metadata (e.g., class descriptions, text captions) to form the similarity graph.
3. Scalability Across Datasets: The $\mathbb{X}$-CLR was tested on datasets of varying scales: ImageNet-1k, CC3M, and CC12M, enabling a comprehensive evaluation of its effectiveness.

Experimental Results

The empirical validations cover three primary scales of datasets:

1. ImageNet-1k: When pretrained on ImageNet-1k, $\mathbb{X}$-CLR outperformed both SimCLR and Supervised Contrastive baselines. Specifically, it showed a 12.4% improvement over SimCLR and a 1.2% improvement over Supervised Contrastive on ImageNet classification tasks, also demonstrating superior performance in image decomposition and object-background separation tasks.
2. CC3M: In the context of the 3-million sample CC3M, $\mathbb{X}$-CLR significantly outperformed CLIP, particularly in lower-data regimes, demonstrating a 16.8% improvement on ImageNet and 18.1% on ImageNet Real. This indicates a strong capability to leverage inter-sample similarities even with noisier data.
3. CC12M: Even with the larger 12-million sample CC12M, $\mathbb{X}$-CLR maintained its effectiveness, showing a 0.6% improvement over CLIP on both ImageNet and ImageNet Real. Moreover, $\mathbb{X}$-CLR demonstrated better data efficiency and representation richness, particularly in tasks requiring fine-grained disambiguation.

Implications and Future Directions

The primary implication of $\mathbb{X}$-CLR is a robust methodology for capturing extensive sample relationships, leading to more generalized and data-efficient model training. This has several practical and theoretical implications:

• Enhanced Representation Learning: The method integrates semantic relationships directly into the training objective, yielding richer and more accurate representations that generalize better across tasks.
• Improved Performance in Low-Data Regimes: $\mathbb{X}$-CLR's ability to leverage cross-sample similarities becomes particularly valuable when training data is scarce, making it useful in scenarios where data collection is expensive or impractical.
• Potential for Integration with Other Models: While the paper primarily focuses on contrastive models, the proposed similarity graph perspective can potentially enhance non-contrastive methods such as BYOL or VICReg, broadening its applicability.

Conclusion

The $\mathbb{X}$-CLR presents a substantive advancement in contrastive learning by addressing the binary limitation of traditional objectives. By incorporating soft similarity into the learning objective, the method achieves superior performance across various datasets and tasks, highlighting the potential of more nuanced inter-sample relationship modeling in enhancing the quality of learned representations. As researchers build on these insights, we can expect further innovations in representation learning methodologies, particularly in their application to multimodal and self-supervised contexts.