BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning

Abstract: Despite the success of deep neural networks, there are still many challenges in deep representation learning due to the data scarcity issues such as data imbalance, unseen distribution, and domain shift. To address the above-mentioned issues, a variety of methods have been devised to explore the sample relationships in a vanilla way (i.e., from the perspectives of either the input or the loss function), failing to explore the internal structure of deep neural networks for learning with sample relationships. Inspired by this, we propose to enable deep neural networks themselves with the ability to learn the sample relationships from each mini-batch. Specifically, we introduce a batch transformer module or BatchFormer, which is then applied into the batch dimension of each mini-batch to implicitly explore sample relationships during training. By doing this, the proposed method enables the collaboration of different samples, e.g., the head-class samples can also contribute to the learning of the tail classes for long-tailed recognition. Furthermore, to mitigate the gap between training and testing, we share the classifier between with or without the BatchFormer during training, which can thus be removed during testing. We perform extensive experiments on over ten datasets and the proposed method achieves significant improvements on different data scarcity applications without any bells and whistles, including the tasks of long-tailed recognition, compositional zero-shot learning, domain generalization, and contrastive learning. Code will be made publicly available at https://github.com/zhihou7/BatchFormer.

• The paper introduces BatchFormer, which enhances representation learning by exploring inter-sample relationships within mini-batches.
• The methodology leverages transformer encoders to connect sparse sample classes, improving performance on tasks like long-tailed recognition and zero-shot learning.
• Empirical evaluations on over ten datasets show improvements up to 2.8% in tail-class accuracy, validating the approach's robustness.

Overview of "BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning"

The paper, "BatchFormer: Learning to Explore Sample Relationships for Robust Representation Learning," presents an intriguing enhancement to deep representation learning in scenarios of data scarcity. The authors introduce BatchFormer, a novel batch transformer module, designed to effectively explore and leverage inter-sample relationships within each mini-batch during training. This approach is notably pertinent for tasks such as long-tailed recognition, zero-shot learning, domain generalization, and contrastive learning, where traditional deep learning methods struggle with insufficient data representation.

Contributions and Methodology

The key contribution of the paper is the introduction of BatchFormer, a transformer-based module applied across the batch dimension of mini-batches. The underlying motivation is to utilize transformer encoders to implicitly learn and exploit relationships between different samples concurrently in a batch. This mechanism allows for collaborative learning among the samples, thereby enhancing the learning process of models dealing with unbalanced, diverse, or domain-specific data distributions.

By sharing classifiers with and without BatchFormer during training but excluding the module in testing, the authors aim to bridge the generalization gap between these two phases. This design ensures that the learned representations are robust, even in the absence of explicitly modeled relationships during deployment. The BatchFormer influences the training dynamics by introducing additional gradient paths between samples through its internal structure, which aids classes with sparse samples.

Empirical Evaluation and Results

The experimental results, spanning over ten datasets, demonstrate substantial improvements in performance across various tasks plagued by data scarcity. For instance, on datasets with long-tailed distributions like ImageNet-LT and iNaturalist 2018, the module exhibits enhanced recognition capabilities for underrepresented classes. Specific numerical gains include improvements in tail-class accuracy by margins of up to 2.8% on ImageNet-LT when using the BatchFormer-enhanced RIDE configuration. Similarly, in zero-shot learning benchmarks, BatchFormer consistently increases both the harmonic mean and unseen class performance metrics.

Implications and Theoretical Insights

The introduction of BatchFormer has both theoretical and practical implications for robust representation learning. Theoretically, this work provides insights into leveraging transformer-based architectures beyond their typical spatial and temporal domains, adapting them instead to capture relational features within batch dimensions. Practically, the paper demonstrates how such internal modeling of sample relationships can be straightforwardly integrated into existing deep learning frameworks, thus enhancing model performance in common yet challenging data scarcity scenarios.

Future Prospects in AI Developments

Looking forward, the methodology encourages exploring hybrid architectures where transformers extend their efficacy across varied dimensions of data. It opens new pathways for improved batch-based learning strategies and adaptive training techniques that could minimize the need for extensive data augmentation. Additionally, further exploration into the scalability of BatchFormer across larger batch dimensions and deeper transformer layers may yield insights into optimal configurations for diverse tasks and datasets.

In conclusion, this paper contributes a practical and theoretical advancement by employing transformer models in a novel context of batch sample relationship exploration, providing a meaningful step towards more resilient AI systems in data-constrained environments.