Semi-supervised Domain Adaptation via Minimax Entropy (1904.06487v5)

Abstract: Contemporary domain adaptation methods are very effective at aligning feature distributions of source and target domains without any target supervision. However, we show that these techniques perform poorly when even a few labeled examples are available in the target. To address this semi-supervised domain adaptation (SSDA) setting, we propose a novel Minimax Entropy (MME) approach that adversarially optimizes an adaptive few-shot model. Our base model consists of a feature encoding network, followed by a classification layer that computes the features' similarity to estimated prototypes (representatives of each class). Adaptation is achieved by alternately maximizing the conditional entropy of unlabeled target data with respect to the classifier and minimizing it with respect to the feature encoder. We empirically demonstrate the superiority of our method over many baselines, including conventional feature alignment and few-shot methods, setting a new state of the art for SSDA.

• The paper introduces a novel minimax entropy method that leverages limited labeled target instances to extract domain-invariant features.
• It employs an adversarial two-step optimization by maximizing conditional entropy on unlabeled targets while minimizing it for the feature encoder.
• Experimental results on DomainNet with ResNet show significant error reductions in both one-shot and three-shot adaptation scenarios.

Semi-supervised Domain Adaptation via Minimax Entropy

This paper explores the semi-supervised domain adaptation (SSDA) problem, where existing methods aligning source and target feature distributions falter in the presence of a few labeled target instances. The authors propose a novel Minimax Entropy (MME) approach designed to leverage these labeled instances effectively, advancing the state of the art in SSDA.

Methodological Overview

The proposed MME method hinges on a well-structured interplay between a feature encoding network and a classification layer, employing an adversarial minimax strategy. The classifier estimates 'prototypes' or representative points for each class, facilitating the extraction of discriminative, domain-invariant features through entropy manipulation. The crux of the adaptation process involves a two-step adversarial optimization: maximizing the conditional entropy of unlabeled target features with respect to the classifier while minimizing it concerning the feature encoder.

Experimental Findings

Empirical results underscore MME’s effectiveness, illustrating significant accuracy improvements over baseline methods, including those relying solely on feature alignment. In various benchmark datasets, MME consistently outperformed traditional methods with notable error reductions. For instance, on the DomainNet dataset with the ResNet architecture, MME reduced error rates compared to alternatives by marked percentages in both one-shot and three-shot scenarios.

Theoretical Insights

The paper connects MME's approach to theoretical frameworks involving domain divergence using the concept of $\mathcal{H}$-divergence, illustrating how the minimax strategy implicitly aligns feature representations across domains. This is achieved by dynamically adjusting the classifier's parameters to induce domain-invariant prototypes, mitigating performance drops due to domain shifts.

Implications and Future Perspectives

From a practical standpoint, MME's success opens avenues for more robust domain adaptation methods in scenarios where labeled data is sparse but critical. Theoretically, it pushes forward the understanding of mitigating domain divergence through adversarial processes. Looking ahead, the approach provides a compelling blueprint for advancing few-shot learning and domain adaptation techniques. One could consider extending this work by exploring different architectures or integrating unsupervised learning paradigms to enhance the flexibility and robustness of the adaptation process.

The paper’s contribution lies in its innovative use of entropy manipulation, representing a significant step in honing machine learning models’ capacity to adapt to new environments with limited supervision. This work not only sets a new performance benchmark but also invites further exploration into domain adaptation's subtleties, potentially influencing approaches across various application areas in AI.