Targeted Representation Alignment for Open-World Semi-Supervised Learning

Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data, while unlabeled samples are not only from the labeled known categories but also from novel categories previously unseen. Despite the promise, current approaches solely rely on hazardous similarity-based clustering algorithms and give unlabeled samples free rein to spontaneously group into distinct novel class clusters. Nevertheless, due to the absence of novel class supervision, these methods typically suffer from the representation collapse dilemma---features of different novel categories can get closely intertwined and indistinguishable, even collapsing into the same cluster and leading to degraded performance. To alleviate this, we propose a novel framework TRAILER which targets to attain an optimal feature arrangement revealed by the recently uncovered neural collapse phenomenon. To fulfill this, we adopt targeted prototypes that are pre-assigned uniformly with maximum separation and then progressively align the representations to them. To further tackle the potential downsides of such stringent alignment, we encapsulate a sample-target allocation mechanism with coarse-to-fine refinery that is able to infer label assignments with high quality. Extensive experiments demonstrate that TRAILER outperforms current state-of-the-art methods on generic and fine-grained benchmarks. The code is available at https://github.com/Justherozen/TRAILER.