A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification

arXiv:2403.18407v1 Announce Type: new
Abstract: Semi-supervised learning (SSL) is a practical challenge in computer vision. Pseudo-label (PL) methods, e.g., FixMatch and FreeMatch, obtain the State Of The Art (SOTA) performances in SSL. These approaches employ a threshold-to-pseudo-label (T2L) process to generate PLs by truncating the confidence scores of unlabeled data predicted by the self-training method. However, self-trained models typically yield biased and high-variance predictions, especially in the scenarios when a little labeled data are supplied. To address this issue, we …

abstract art arxiv challenge classification computer computer vision confidence cs.ai cs.cv ensemble generate labels low performances practical process semi-supervised semi-supervised learning sota ssl state state of the art supervised learning threshold type unbiased variance vision