Instance Adaptive Self-Training for Unsupervised Domain Adaptation (2008.12197v1)

Abstract: The divergence between labeled training data and unlabeled testing data is a significant challenge for recent deep learning models. Unsupervised domain adaptation (UDA) attempts to solve such a problem. Recent works show that self-training is a powerful approach to UDA. However, existing methods have difficulty in balancing scalability and performance. In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation. To effectively improve the quality of pseudo-labels, we develop a novel pseudo-label generation strategy with an instance adaptive selector. Besides, we propose the region-guided regularization to smooth the pseudo-label region and sharpen the non-pseudo-label region. Our method is so concise and efficient that it is easy to be generalized to other unsupervised domain adaptation methods. Experiments on 'GTA5 to Cityscapes' and 'SYNTHIA to Cityscapes' demonstrate the superior performance of our approach compared with the state-of-the-art methods.

Instance Adaptive Self-Training for Unsupervised Domain Adaptation: An Expert Review

The paper "Instance Adaptive Self-Training for Unsupervised Domain Adaptation" presents a comprehensive framework to enhance unsupervised domain adaptation (UDA) for the task of semantic segmentation. Semantic segmentation, a critical task in computer vision, involves labeling each pixel in an image with its respective category. The challenge addressed here is the domain shift: the divergence between the labeled source domain and the unlabeled target domain. This shift often results in a significant performance drop when models trained on the source domain are applied directly to the target domain.

Core Contributions and Methodology

This paper introduces the Instance Adaptive Self-Training (IAST) framework that employs a self-training strategy for UDA. The novelty in this framework is two-fold: the Instance Adaptive Selector (IAS) for pseudo-label generation and the region-guided regularization approach.

1. Instance Adaptive Selector (IAS): The authors describe a mechanism to improve pseudo-label quality by dynamically adjusting the threshold for pseudo-label generation on a per-instance basis. This adaptive mechanism considers both global and instance-specific confidence levels to enhance the diversity and accuracy of the pseudo-labels. A unique hard class weight decay strategy further refines this by reducing redundancy and increasing diversity in pseudo-labels, which is particularly beneficial for harder classes that are prone to noise.
2. Region-Guided Regularization: The framework divides the pseudo-label space into confidence and ignored regions, applying different regularization strategies to each. The confident region benefits from Kullback-Leibler Divergence (KLD) minimization to mitigate overfitting to noise, while the ignored region uses entropy minimization to encourage sharper predictions without supervision.

Numerical Results

The IAST framework has undergone extensive evaluation on recognized synthetic-to-real semantic segmentation benchmarks such as GTA5 to Cityscapes and SYNTHIA to Cityscapes. The framework significantly outperformed state-of-the-art approaches with a mean Intersection over Union (mIoU) improvement, demonstrating its robust applicability to domain adaptation tasks. For instance, IAST achieved a mIoU of 52.2% on the GTA5 to Cityscapes benchmark, notably surpassing the previous mixed-methods approaches, which combine adversarial and self-training methods.

Practical and Theoretical Implications

The proposed IAST framework offers a highly scalable and flexible solution with minimal dependencies, making it applicable across various UDA scenarios and adaptable as an extension to any existing semantic segmentation strategies. This characteristic ensures that the framework can be integrated easily into existing architectures, providing performance benefits across different domain adaptation contexts.

Moreover, the paper demonstrates applicability beyond UDA, extending to semi-supervised learning environments. It achieves state of the art in semi-supervised semantic segmentation tasks on the Cityscapes dataset, showcasing the utility of self-training approaches in scenarios with partially labeled data.

Future Developments

While the IAST framework exhibits significant improvements over existing methods, future work could aim to optimize computational efficiency further, especially in large-scale real-time applications. Additionally, exploring the theoretical underpinnings of adaptive self-training dynamics in varying domain adaptation contexts can offer deeper insights into the nature of domain shifts and how to best mitigate them.

In summary, the Instance Adaptive Self-Training framework establishes a potent method for enhancing semantic segmentation tasks in the face of domain shifts, presenting both practical innovations and setting a new benchmark in the domain adaptation arena.