Learning Not to Learn: Training Deep Neural Networks with Biased Data

Abstract: We propose a novel regularization algorithm to train deep neural networks, in which data at training time is severely biased. Since a neural network efficiently learns data distribution, a network is likely to learn the bias information to categorize input data. It leads to poor performance at test time, if the bias is, in fact, irrelevant to the categorization. In this paper, we formulate a regularization loss based on mutual information between feature embedding and bias. Based on the idea of minimizing this mutual information, we propose an iterative algorithm to unlearn the bias information. We employ an additional network to predict the bias distribution and train the network adversarially against the feature embedding network. At the end of learning, the bias prediction network is not able to predict the bias not because it is poorly trained, but because the feature embedding network successfully unlearns the bias information. We also demonstrate quantitative and qualitative experimental results which show that our algorithm effectively removes the bias information from feature embedding.

• The paper introduces a mutual information regularization loss that reduces the model's reliance on biased features.
• Using supervised parameterization, the method refines feature embeddings to better align with unbiased conditional distributions.
• Empirical evaluations on datasets like IMDB Face and MNIST demonstrate significantly improved generalization compared to standard approaches.

Overview of "Learning Not to Learn: Training Deep Neural Networks with Biased Data"

The paper "Learning Not to Learn: Training Deep Neural Networks with Biased Data" by Kim et al. presents a study on improving the generalization capability of deep neural networks trained on biased datasets. This work primarily focuses on minimizing the dependency of neural networks on biased features by leveraging mutual information as a regularization strategy.

Core Contributions

The central contribution of this paper is the proposition of a regularization loss, $\mathcal{L}_{MI}(\theta_f,\theta_h)$, aimed at reducing biases in model training. The authors introduce a method to minimize mutual information between feature embeddings and identified biases. This principle necessitates the reformulation of the conditional entropy component, guiding the models to diminish the influence of bias-related information during training.

The authors parametrically adjust the $Q_{b(X)|f(X)}(b|f)$ distributions through supervised learning techniques. This approach facilitates the learning of mappings that are closely aligned with the unbiased conditional distributions through parameter $\theta_h$. The regularization loss is carefully designed with an additional Kullback-Leibler divergence term to force the learning models to minimize biased learning behaviors.

Empirical Evaluation and Results

The effectiveness of the proposed method is demonstrated on datasets where bias is either empirically identified or intentionally introduced. For instance, the IMDB Face dataset was meticulously cleaned to exclude noisy instances, ensuring a robust evaluation of models' bias reliance. Similarly, the MNIST dataset was artistically altered with color biases across digit categories to simulate bias effects. The selection of distinct colors assigned to digits within training datasets vividly illustrates how biases can be engineered and tested in a controlled setting.

Numerical results exhibit that models trained with the proposed regularization technique achieve better generalization performance on biased datasets compared to baseline approaches. The incorporation of the regularization loss allowed models to focus less on the bias-specific features, thereby enhancing their operational efficacy on unbiased test sets.

Implications and Future Directions

The implications of this research are profound for practical application areas where biased data is prevalent. In typical deployment scenarios, datasets often contain inadvertent biases, limited by specific demographic or environmental constraints. The method outlined in this paper offers a strategic enhancement to counteract such biases, paving the way for more equitable and reliable machine learning models.

From a theoretical standpoint, the usage of mutual information to guide learning contributes valuable insights into addressing the long-standing challenge of bias in statistical learning. As AI continues to proliferate across domains, methodologies that inherently diminish model bias could become integral to system design, ensuring ethical and fair deployment.

Looking forward, this research opens new avenues for investigating similar regularization strategies in various contexts, such as transfer learning, unsupervised domain adaptation, and reinforcement learning. The integration and adaptation of such techniques in more complex, multi-modal datasets stand as a compelling subject for future inquiries. Additionally, exploring the mathematical underpinnings and limits of mutual information constraints in contemporary large-scale architectures could yield further progress in bias-minimized learning frameworks.

In summary, Kim et al.'s work presents a methodologically sound and empirically validated approach to mitigating bias in deep learning through an innovative regularization loss paradigm, contributing significantly to the discourse on ethical AI practices.