Self-Challenging Improves Cross-Domain Generalization (2007.02454v1)

Abstract: Convolutional Neural Networks (CNN) conduct image classification by activating dominant features that correlated with labels. When the training and testing data are under similar distributions, their dominant features are similar, which usually facilitates decent performance on the testing data. The performance is nonetheless unmet when tested on samples from different distributions, leading to the challenges in cross-domain image classification. We introduce a simple training heuristic, Representation Self-Challenging (RSC), that significantly improves the generalization of CNN to the out-of-domain data. RSC iteratively challenges (discards) the dominant features activated on the training data, and forces the network to activate remaining features that correlates with labels. This process appears to activate feature representations applicable to out-of-domain data without prior knowledge of new domain and without learning extra network parameters. We present theoretical properties and conditions of RSC for improving cross-domain generalization. The experiments endorse the simple, effective and architecture-agnostic nature of our RSC method.

An Analysis of "Self-Challenging Improves Cross-Domain Generalization"

The paper "Self-Challenging Improves Cross-Domain Generalization" introduces a novel training method called Representation Self-Challenging (RSC) designed to enhance the generalization capabilities of Convolutional Neural Networks (CNNs) to out-of-domain data. It addresses the challenge posed by the discrepancy in performance when CNNs are tested on data from distributions differing from those they were trained on.

Methodology

RSC is a training heuristic that iteratively inhibits the dominant features in the feature maps activated by CNNs during training. By discarding these features, RSC compels the network to utilize a broader set of features to make predictions. This approach does not rely on prior knowledge of new domains nor does it require additional network parameters. The method is theoretically grounded in the idea that leveraging a more comprehensive set of features across varying distributions can lead to improved generalization.

Theoretical and Empirical Results

The paper provides both theoretical analysis and empirical evaluations to support the efficacy of RSC. Theoretically, RSC is shown to induce a decreased generalization bound, suggesting an improvement in the network's ability to generalize. Empirically, the method was tested on several cross-domain generalization benchmarks, such as PACS, VLCS, Office-Home, and ImageNet-Sketch, where it demonstrated notable performance improvements. For instance, when tested on the PACS dataset, RSC showed consistent gains, particularly in domains where color information was less informative (e.g., sketches).

Comparisons and Ablations

RSC is compared against other regularization techniques and dropout methods, such as Cutout, DropBlock, and Adversarial Dropout. The results indicate that RSC's gradient-based feature selection process is more effective than methods relying on randomness or maximizing prediction divergence. Additionally, the paper explores variations of RSC, such as spatial-wise and channel-wise implementations, and provides empirical guidance on hyperparameter settings.

Implications and Future Work

The findings suggest that RSC is a scalable, architecture-agnostic method that can be easily integrated into existing CNN training pipelines to boost generalization performance across multiple domains. The implications extend to practical applications, where models trained with RSC can be more robustly deployed in environments where new data distributions are encountered.

Future work could explore the integration of RSC with more complex architectures and its application in real-world scenarios requiring robust cross-domain capabilities. Further studies on hyperparameter tuning and understanding the underlying mechanisms of RSC could yield additional insights into its optimal use.

In conclusion, RSC represents a substantial contribution to the field of domain generalization, offering both a theoretical framework and practical methodology for enhancing the adaptability of CNNs in diverse and dynamic data environments.