Stochastic Binary Network for Universal Domain Adaptation

Saurabh Kumar Jain, Sukhendu Das; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 107-116

Universal domain adaptation (UniDA) is the unsupervised domain adaptation with label shift. UniDA aims to classify unlabeled target samples into one of the "known" categories or into a single "unknown" category. Its main challenge lies in detecting private classes from both domains and performing alignment between the common classes. Current methods employ various techniques and loss functions to address these challenges. However, these methods commonly represent classifiers as point weight vectors, which are prone to overfitting by the source domain samples due to the lack of supervision from the target domain. Consequently, these classifiers struggle to separate target samples into known and unknown categories effectively. To address this, we introduce a novel framework called Stochastic Binary Network for Universal Domain Adaptation (STUN). STUN uses a Stochastic binary classifier for each class, whose weight is modeled as Gaussian distribution, enabling to sample an arbitrary number of classifiers while keeping the model size same as of two classifiers. Consistency between these sampled classifiers is used to derive the confidence scores for both source and target samples, which facilitates the alignment of common classes using weighted adversarial learning. Finally, we use deep discriminative clustering to formulate a loss function for solving the problem of fragmented feature distributions in the target domain. Extensive ablation studies and state-of-the-art results across three standard benchmark datasets show the efficacy of our framework.