Reducing Overfitting in Deep Networks by Decorrelating Representations

Abstract: One major challenge in training Deep Neural Networks is preventing overfitting. Many techniques such as data augmentation and novel regularizers such as Dropout have been proposed to prevent overfitting without requiring a massive amount of training data. In this work, we propose a new regularizer called DeCov which leads to significantly reduced overfitting (as indicated by the difference between train and val performance), and better generalization. Our regularizer encourages diverse or non-redundant representations in Deep Neural Networks by minimizing the cross-covariance of hidden activations. This simple intuition has been explored in a number of past works but surprisingly has never been applied as a regularizer in supervised learning. Experiments across a range of datasets and network architectures show that this loss always reduces overfitting while almost always maintaining or increasing generalization performance and often improving performance over Dropout.

• The paper introduces the DeCov regularizer that reduces overfitting by minimizing hidden activation cross-covariance in deep networks.
• Experiments on MNIST, CIFAR, and ImageNet show narrowed training-validation gaps and up to 4.5% test accuracy improvement over baseline models.
• The approach highlights the benefit of reducing feature redundancy and suggests potential integration with other methods like Dropout for enhanced generalization.

Reducing Overfitting in Deep Networks by Decorrelating Representations

The paper entitled "Reducing Overfitting in Deep Networks by Decorrelating Representations" introduces a new regularization technique named DeCov, aimed at mitigating overfitting in Deep Neural Networks (DNNs) by minimizing cross-covariance among hidden activations. The authors propose that by promoting non-redundant representations, the DeCov loss can enhance the generalization ability of DNNs beyond existing methods like Dropout.

Core Contributions

The primary innovation of the paper is the introduction of the DeCov regularizer, which minimizes the cross-covariance of hidden unit activations. This approach is grounded on insights from classical machine learning theories emphasizing uncorrelated features, and it is argued to act akin to model averaging, reducing co-adaptation among activations—a central concept addressed by Dropout but through a different mechanism.

The authors conducted experiments across several datasets (MNIST, CIFAR10, CIFAR100, ImageNet) and architectures (AlexNet, LeNet, Network in Network) to validate DeCov's efficacy. These experiments consistently demonstrated reductions in overfitting, evidenced by narrower gaps between training and validation accuracies and, in numerous cases, improvements over Dropout in test set performance.

Numerical Results and Observations

The DeCov approach reveals significant numeric results across various experimental setups:

• On CIFAR10, DeCov alone improved test accuracy by approximately 4.5% compared to a baseline without any regularization, with a notable decrease in the training-validation performance gap.
• On CIFAR100, a combination of Dropout and DeCov yielded the highest test accuracy, with the setup achieving a 34% reduction in the train-test gap compared to the non-regularized baseline.
• Within the high-dimensional ImageNet experiments, DeCov consistently reduced the overfitting observed in conventional setups, with marked improvements in both Top-1 and Top-5 accuracy metrics.

Theoretical and Practical Implications

The research illustrates a compelling extension of regularization tactics in deep learning, suggesting that explicitly modeling covariance relationships between neuron activations can yield powerful regularization effects. The key implication is that neural networks, particularly those of high capacity, benefit from architectural mechanisms that inherently promote decorrelation in representations.

Practically, this approach invites deeper investigation into alternative forms of regularization that synergize or potentially replace existing methods such as Dropout, especially in large-scale models and diverse domains. It inspires a perspective that links broader ensemble learning theories with neural network training strategies, suggesting a fertile area for further exploration.

Speculations on Future Developments

The paper opens several avenues for future inquiry and development:

• The integration of DeCov with other regularization and optimization methods might lead to compounded benefits in robustness and generalization.
• Exploring applications of DeCov in supervised, unsupervised, and transfer learning contexts could provide insights into its wider applicability outside the supervised domains tested.
• Investigating the trade-offs between computational complexity and performance gains with DeCov will be critical for its adoption in real-world systems, especially where computational resources are limited.

In conclusion, by presenting a flexible regularizer that leverages covariance minimization, this work contributes methodologically to the domain of deep learning, offering a novel tool for researchers and practitioners striving to enhance model generalization capabilities.