Efficient Cross-Modality Knowledge Distillation with Contrastive Learning

Keskeiset käsitteet

The core message of this paper is to propose a generalizable cross-modality contrastive distillation (CMCD) framework that leverages contrastive learning to effectively distill knowledge from a source modality (e.g., image) to a target modality (e.g., sketch) without requiring labeled data in the target modality. The authors also provide a theoretical analysis that reveals the connection between the performance of the algorithm and the distance between the source and target modalities.

Tiivistelmä

The paper presents a framework called Cross-Modality Contrastive Distillation (CMCD) for efficiently transferring knowledge from a source modality (e.g., image) to a target modality (e.g., sketch) without requiring labeled data in the target modality. The key aspects of the methodology are:

Contrastive Learning on Source Modality:

The authors first use contrastive learning (e.g., SimCLR) to learn a feature extractor ϕA on the unlabeled source modality data.

Cross-Modality Distillation:

The authors propose two types of cross-modality distillation losses:

Cross-Modality Distillation (CMD) loss: Distills the knowledge from the source modality to the target modality by minimizing the difference between the contrastive distributions of the two modalities.
Cross-Modality Contrastive (CMC) loss: Aligns the feature representations of the source and target modalities using a contrastive objective.

These losses are used to train a feature extractor ϕB on the target modality using a small amount of paired data between the source and target modalities.

Downstream Task Fine-tuning:

The learned feature extractor ϕB is then used to fine-tune a simple classifier (e.g., MLP) on the downstream task in the target modality using a small amount of labeled data.

The authors also provide a theoretical analysis that shows the final test error in the target modality is bounded by the total variation distance between the source and target modality distributions, as well as the Rademacher complexities of the feature extractor and classifier models. This theoretical insight is validated by the experimental results, which demonstrate the effectiveness of the proposed CMCD framework across various cross-modality tasks and datasets.

Tilastot

The distance between the source and target modality distributions significantly impacts the test error on downstream tasks within the target modality.
The Rademacher complexities of the feature extractor and classifier models also contribute to the final test error bound.

Lainaukset

"Cross-modality distillation arises as an important topic for data modalities containing limited knowledge such as depth maps and high-quality sketches."
"To bridge the gap between the theory and practical method of cross-modality distillation, we first formulate a general framework of cross-modality contrastive distillation (CMCD), built upon contrastive learning that leverages both positive and negative correspondence, towards a better distillation of generalizable features."
"Our findings underscore a direct correlation between the algorithm's ultimate performance and the total variation distance between the source and target modalities that is further validated by our empirical results."

Tärkeimmät oivallukset

On the Theory of Cross-Modality Distillation with Contrastive Learning

by Hangyu Lin,C... klo arxiv.org 05-07-2024

https://arxiv.org/pdf/2405.03355.pdf

Syvällisempiä Kysymyksiä

How can the proposed CMCD framework be extended to handle more than two modalities, and what are the theoretical implications of such an extension

To extend the proposed Cross-Modality Contrastive Distillation (CMCD) framework to handle more than two modalities, we can introduce additional loss terms that capture the relationships between multiple modalities. By incorporating contrastive learning across multiple modalities, we can distill generalizable features from a primary source modality to multiple target modalities simultaneously. The theoretical implications of such an extension would involve analyzing the interactions and dependencies between the different modalities, leading to a more complex convergence analysis. The total variation distance between all pairs of modalities would play a crucial role in bounding the test error on downstream tasks within each target modality. Additionally, the Rademacher complexities associated with the feature extractors and prediction models of each modality would need to be considered to ensure the generalization bounds hold across multiple modalities.

What are the potential limitations of the contrastive learning approach in the context of cross-modality distillation, and how can they be addressed

One potential limitation of the contrastive learning approach in the context of cross-modality distillation is its reliance on pairwise relationships between samples. This approach may struggle with capturing higher-order relationships or complex interactions between modalities that go beyond pairwise comparisons. To address this limitation, one possible solution is to incorporate higher-order statistics or relationships into the contrastive loss function. By considering more complex relationships between samples in different modalities, the model can learn more nuanced and informative representations. Additionally, leveraging multi-modal pretraining techniques and incorporating domain-specific knowledge can enhance the effectiveness of contrastive learning in capturing cross-modal relationships.

Can the insights from this work be applied to other transfer learning scenarios beyond cross-modality distillation, such as domain adaptation or few-shot learning

The insights from this work on cross-modality distillation can be applied to other transfer learning scenarios, such as domain adaptation and few-shot learning. In domain adaptation, the theoretical analysis of the total variation distance between source and target distributions can guide the design of effective adaptation algorithms. By minimizing the distribution gap between domains, models can transfer knowledge more efficiently. Similarly, in few-shot learning, the concept of distilling generalizable features from a rich modality to a limited modality can be leveraged to improve the performance of models with limited labeled data. By transferring knowledge from a source modality with abundant data to a target modality with limited data, models can generalize better in few-shot learning scenarios.