Adversarial Robustness as a Prior for Learned Representations (1906.00945v2)

Abstract: An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Our code and models for reproducing these results is available at https://git.io/robust-reps .

• The paper demonstrates that adversarially robust models learn more interpretable and approximately invertible feature representations than standard networks.
• It empirically verifies that robust models, despite a slight drop in standard accuracy, perform significantly better under adversarial settings.
• The study highlights that using adversarial robustness as a prior enhances semantic feature quality, enabling practical feature visualization and manipulation.

Analysis of "Adversarial Robustness as a Prior for Learned Representations"

The paper "Adversarial Robustness as a Prior for Learned Representations" addresses the limitations of feature representations learned by standard deep neural networks. Through robust optimization, the authors propose a method that enforces priors on these representations, ultimately yielding more interpretable and human-aligned features. This approach utilizes the framework of adversarial training to achieve higher-quality learned embeddings.

Key Contributions

1. Robust Representations: The seminal contribution of this research is the demonstration that adversarially robust models inherently learn better feature representations compared to standard networks. The authors illustrate that robust representations develop embeddings that facilitate direct visualization, approximately invertible mappings, and potential for manipulation—all without the need for additional regularization.
2. Inversion and Visualization: The paper illustrates that robust representations are approximately invertible, enabling the recovery or visualization of semantically consistent features from learned embeddings. This significantly contrasts with standard embeddings where such inversion fails to retrieve human-interpretable features.
3. Feature Manipulation: Building on the robust representations' properties, the paper introduces methods for feature manipulation. By directly optimizing the representations' components, one can add or modify salient features in the input space, thereby offering new avenues for deep model interpretability and interaction.
4. Empirical Evidence: The paper provides a comprehensive empirical evaluation across diverse datasets, including ImageNet and a reduced version termed Restricted ImageNet. These experiments demonstrate the practical advantages of robust optimization, with robust models outperforming their standard counterparts in terms of feature alignments with human perception.

Numerical Outcomes and Observations

The robust models, while exhibiting a moderate drop in standard accuracy (notably 92.39% compared to 98.01% for standard models in Restricted ImageNet), excel significantly under adversarial settings with an adversarial accuracy of 81.91% against 4.74% for standard ones. The evidence suggests that the trade-off in standard accuracy is vastly outweighed by the qualitative gains in interpretability and robustness.

Furthermore, the robust models facilitate direct and visible interpolation between representations, enabling smooth transitions between various semantic features within the representation space. This characteristic provides an innovative tool for generating realistic and plausible data augmentations or transformations.

Implications and Future Directions

The findings highlight the role of adversarial robustness beyond security applications—serving as a guiding principle for improving feature representations. This positioning invites further exploration into structuring robust classifiers not solely for robustness but also for perceptual quality.

The approach opens the prospect of leveraging adversarial robustness to direct model learning towards more human-like perception alignment, crucial for fields such as autonomous systems, medical imaging, and human-computer interaction.

Future research may explore varying robustness objectives, potentially integrating more complex or domain-specific priors. Furthermore, investigating the interplay between robustness, interpretability, and other performance metrics in more challenging and diverse settings would be beneficial.

In conclusion, the paper proposes a meaningful shift in thinking about adversarial robustness—transforming it from a defensive mechanism into an influential guiding principle for developing robust feature representations in neural networks. This shift could play a crucial part in shaping the design and functionality of future deep learning models.