i-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning (2010.08887v2)

Abstract: Contrastive representation learning has shown to be effective to learn representations from unlabeled data. However, much progress has been made in vision domains relying on data augmentations carefully designed using domain knowledge. In this work, we propose i-Mix, a simple yet effective domain-agnostic regularization strategy for improving contrastive representation learning. We cast contrastive learning as training a non-parametric classifier by assigning a unique virtual class to each data in a batch. Then, data instances are mixed in both the input and virtual label spaces, providing more augmented data during training. In experiments, we demonstrate that i-Mix consistently improves the quality of learned representations across domains, including image, speech, and tabular data. Furthermore, we confirm its regularization effect via extensive ablation studies across model and dataset sizes. The code is available at https://github.com/kibok90/imix.

Overview of $i$-Mix for Contrastive Representation Learning

The authors present a domain-agnostic strategy named $i$-Mix, aimed at enhancing the learning process of contrastive representations across various domains. The paper focuses on self-supervised learning tasks where labeled data are sparse or nonexistent, positioning the contrastive learning framework as an efficient method to automatically derive meaningful data representations. While past research has predominantly targeted specific domains through tailored data augmentation techniques, this paper proposes a general approach that does not rely on domain-specific knowledge. Contrastive learning's efficacy hinges on the ability to differentiate between positive and negative samples of data representations within a batch, typically utilizing data augmentation designed specifically for the domain.

Methodology

$i$-Mix innovatively extends the MixUp methodology to the domain of contrastive learning. By mixing data instances alongside their virtual labels, $i$-Mix introduces a technique to improve the variety and the robustness of representations learned during the training process. The procedure involves creating virtual classes by assigning a unique identity to each data instance without requiring explicit labels, enabling the mixing of data within both input and label spaces.

The authors extend the method across several state-of-the-art contrastive learning paradigms:

• SimCLR: $i$-Mix utilizes an N-pair contrastive loss formulation to introduce data mixing while efficiently utilizing batch processing.
• MoCo: Employing a memory-augmented approach, $i$-Mix maintains a queue of previous embeddings, integrating mixing strategies to refine representation quality.
• BYOL: This method traditionally does not rely on negative pairs; however, $i$-Mix adopts BYOL's architectural basis for embedding augmentation.

Experimental Results

The experiments demonstrate that $i$-Mix consistently enhances the performance of contrastive learning methods across multiple domains: image, speech, and tabular data. Significant performance improvements are reported over baselines without $i$-Mix, notably achieving classifications on par with fully supervised learning models in some tasks. In particular, when applied to datasets like CIFAR-10 and Speech Commands, $i$-Mix achieves competitive accuracy levels, i.e., significantly augmenting the representation quality when compared to base methods.

Moreover, the researchers have explored the scalability and robustness of $i$-Mix by analyzing its performance across varying model sizes and epoch counts. The paper reveals that $i$-Mix provides strong regularization benefits particularly advantageous for smaller datasets or datasets lacking comprehensive knowledge on optimal data augmentation strategies. The experimental settings further indicate that deeper models benefit more substantially from $i$-Mix application, where longer training periods ensure better generalization and less susceptibility to overfitting.

Implications and Future Directions

The implications of adopting $i$-Mix in contrastive representation learning are notable given the method’s versatility across different domains without the need for domain-specific customization. The technique shows promise in enhancing existing self-supervised learning processes by offering more robust and generalizable representations, which could be pivotal in domains where acquiring labeled data is costly or impractical.

For future developments, $i$-Mix provides a basis upon which further domain-independent strategies can be developed specifically for emerging fields within AI and ML. Extending this approach to even more complex multi-modal datasets could open doors for significant advancements in machine perception and autonomous decision-making systems. Future work could also optimize the computational efficiency of $i$-Mix to make it more feasible for use in large-scale industrial applications where computational resources are a limiting factor. Overall, $i$-Mix represents an important step towards generically applicable solutions in the self-supervised learning landscape.