MEMO: Test Time Robustness via Adaptation and Augmentation (2110.09506v3)

Abstract: While deep neural networks can attain good accuracy on in-distribution test points, many applications require robustness even in the face of unexpected perturbations in the input, changes in the domain, or other sources of distribution shift. We study the problem of test time robustification, i.e., using the test input to improve model robustness. Recent prior works have proposed methods for test time adaptation, however, they each introduce additional assumptions, such as access to multiple test points, that prevent widespread adoption. In this work, we aim to study and devise methods that make no assumptions about the model training process and are broadly applicable at test time. We propose a simple approach that can be used in any test setting where the model is probabilistic and adaptable: when presented with a test example, perform different data augmentations on the data point, and then adapt (all of) the model parameters by minimizing the entropy of the model's average, or marginal, output distribution across the augmentations. Intuitively, this objective encourages the model to make the same prediction across different augmentations, thus enforcing the invariances encoded in these augmentations, while also maintaining confidence in its predictions. In our experiments, we evaluate two baseline ResNet models, two robust ResNet-50 models, and a robust vision transformer model, and we demonstrate that this approach achieves accuracy gains of 1-8\% over standard model evaluation and also generally outperforms prior augmentation and adaptation strategies. For the setting in which only one test point is available, we achieve state-of-the-art results on the ImageNet-C, ImageNet-R, and, among ResNet-50 models, ImageNet-A distribution shift benchmarks.

• The paper introduces MEMO, a plug-and-play method that enhances test time robustness by adapting model parameters using self-supervised augmentations.
• It leverages single-input augmentations to minimize marginal entropy, achieving 1% to 8% accuracy improvements across CIFAR-10-C, ImageNet-C, and related benchmarks.
• MEMO’s flexibility allows seamless integration with diverse neural networks without modifying training, offering practical gains for real-world applications.

An Analysis of "MEMO: Test Time Robustness via Adaptation and Augmentation"

The paper "MEMO: Test Time Robustness via Adaptation and Augmentation" explores enhancing the performance of deep learning models when faced with input perturbations and domain shifts during test time. The method proposed, termed MEMO, aims to improve the robustness of a model in the face of such distribution shifts, leveraging test time inputs through a process of adaptation and augmentation. This approach is critical as it applies particularly to scenarios where the model is probabilistic and adaptable by working independently on single test inputs without requiring access to multiple points, thereby addressing limitations present in various prior test time adaptation techniques.

Key Methodology

The core concept of MEMO is utilizing data augmentations as a self-supervised learning signal at test time. The method involves performing augmentations on a single test input, followed by adapting the model's parameters to ensure consistent and confident predictions across these augmented inputs. This is achieved by minimizing the marginal entropy of the model's output distribution over the augmentations, which is hypothesized to encourage the model to produce stable predictions that are invariant to the applied augmentations while maintaining prediction confidence.

The simplicity and flexibility of MEMO lie in its broad applicability to any model meeting certain basic differentiable output requirements. The method does not impose modifications on the training process, making it a plug-and-play solution that blends seamlessly with a variety of pre-trained deep neural networks across different architecture types.

Experimental Verification

The authors conduct extensive experiments on standard benchmarks such as CIFAR-10-C, ImageNet-C, ImageNet-R, and ImageNet-A to verify the efficacy of MEMO. They demonstrate that MEMO consistently outperforms some prior methods by margins ranging from 1% to 8% improved accuracy across different datasets. Notably, MEMO achieves several state-of-the-art accuracies for ResNet-50 models and vision transformers under distribution shifts when only one test input is processed at a time.

An ablation paper further substantiates the contribution of MEMO’s aspects—augmentation and adaptation—to the performance gains observed. It shows that the marginal entropy minimization components are vital as they deliver more consistent improvements compared to mere augmentation approaches or alternative adaptation strategies like pairwise cross entropy or conditional entropy minimization.

Implications and Future Work

Practically, MEMO’s ability to enhance deep learning model robustness without necessitating modifications to the training regime can significantly streamline deployment in real-world applications susceptible to input perturbations and domain shifts. The frameworks presented, including combining robust training methods with MEMO for greater performance, highlight potential utility across varied data domains and model architectures.

For future research, MEMO opens avenues for exploring selective adaptation to improve inference efficiency, possibly by aligning the process with model calibration indicators. Moreover, exploring MEMO in settings that allow continual learning during test time, particularly addressing the challenge of avoiding model drift into degenerate solutions, presents fertile ground for further advancement of robust deep learning models.

Conclusion

The MEMO method offers a significant contribution to the field of deep learning by improving test time robustness without additional burdens on model training or deployment infrastructures. Addressing key limitations of existing adaptation methods, MEMO stands out for its versatility and effectiveness, as demonstrated in insights from comprehensive empirical validation across multiple challenging distribution shift benchmarks. As deep learning continues to be integral in dynamic and uncertain environments, methods like MEMO will be crucial to ensuring reliability and robustness in practice.