Deep Stable Learning for Out-Of-Distribution Generalization (2104.07876v1)

Abstract: Approaches based on deep neural networks have achieved striking performance when testing data and training data share similar distribution, but can significantly fail otherwise. Therefore, eliminating the impact of distribution shifts between training and testing data is crucial for building performance-promising deep models. Conventional methods assume either the known heterogeneity of training data (e.g. domain labels) or the approximately equal capacities of different domains. In this paper, we consider a more challenging case where neither of the above assumptions holds. We propose to address this problem by removing the dependencies between features via learning weights for training samples, which helps deep models get rid of spurious correlations and, in turn, concentrate more on the true connection between discriminative features and labels. Extensive experiments clearly demonstrate the effectiveness of our method on multiple distribution generalization benchmarks compared with state-of-the-art counterparts. Through extensive experiments on distribution generalization benchmarks including PACS, VLCS, MNIST-M, and NICO, we show the effectiveness of our method compared with state-of-the-art counterparts.

• The paper introduces StableNet, which decorrelates irrelevant features to mitigate spurious correlations in out-of-distribution scenarios.
• It employs Random Fourier Features alongside a novel sample weighting mechanism to remove nonlinear dependencies during deep model training.
• StableNet outperforms existing methods on benchmarks like PACS, VLCS, MNIST-M, and NICO, demonstrating robust out-of-distribution generalization.

Deep Stable Learning for Out-of-Distribution Generalization: An Expert Analysis

The paper "Deep Stable Learning for Out-Of-Distribution Generalization" addresses the challenges faced by deep learning models when encountering distribution shifts between training and testing data. This situation arises when the independent and identically distributed (i.i.d.) assumption is violated due to various reasons such as data selection biases or confounding factors. The paper introduces StableNet—an innovative approach designed to enhance the generalization abilities of neural networks in out-of-distribution (OOD) scenarios by mitigating spurious correlations between irrelevant features and label outcomes.

Core Contributions

1. Decorrelation of Features: The primary focus of StableNet is the decorrelation of features through a sample weighting mechanism, which alleviates spurious correlations. Unlike traditional approaches that assume domain labels or balanced domain capacities, the proposed method requires neither of these assumptions, making it highly practical for real-world applications where such information is often unavailable.
2. Random Fourier Features (RFF) and Sample Weighting: By leveraging the properties of RFFs alongside a novel sample weighting technique, StableNet aims to remove nonlinear dependencies among features, which is significantly more challenging than linear dependencies. The optimization of sample weights is guided by minimizing these dependencies using the Frobenius norm of a partial cross-covariance matrix.
3. Efficient Training Procedure: Recognizing the computational and storage challenges in applying these techniques to deep models, StableNet incorporates a saving and reloading strategy to manage global feature decorrelation with minimal overhead.

Experimental Evaluation

The efficacy of StableNet is thoroughly validated across diverse and challenging settings:

• Unbalanced Settings: StableNet outperforms state-of-the-art methods across various domain generalization benchmarks like PACS, VLCS, MNIST-M, and NICO. In scenarios where training samples are uneven across latent domains, it consistently shows improved generalization by focusing on true discriminative features over irrelevant correlations.
• Flexible and Adversarial Settings: The method proves resilient even when domain shifts are adversarially crafted or vary significantly between classes. For example, in MNIST-M experiments with backgrounds as dominant contexts inducing adversarial correlations, StableNet achieves superior performance by effectively balancing the contribution of diverse feature components.

Implications and Future Directions

StableNet's ability to mitigate feature dependence without explicit domain labels is a promising tool for enhancing OOD generalization. This method could be a stepping stone towards more sophisticated models that learn invariant representations robust to distribution shifts. Future research could explore:

• Extending the sample weighting framework to accommodate even larger-scale datasets and intricate data types, further optimizing computational efficiency.
• Investigating the integration of StableNet into variational and generative models where disentangled feature learning plays a crucial role.

This paper contributes substantively to the discourse on domain generalization, emphasizing the utility of decorrelation methods in achieving genuine invariance across environments. The development of StableNet marks a progressive step in the pursuit of robust machine learning systems adaptable to the complexities of real-world data distribution.