A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification
<|reference_start|>A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is Critical for Semi-supervised Classification: 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 propose a lightweight channel-based ensemble method to effectively consolidate multiple inferior PLs into the theoretically guaranteed unbiased and low-variance one. Importantly, our approach can be readily extended to any SSL framework, such as FixMatch or FreeMatch. Experimental results demonstrate that our method significantly outperforms state-of-the-art techniques on CIFAR10/100 in terms of effectiveness and efficiency.<|reference_end|>
@article{wu2024a,
    title={A Channel-ensemble Approach: Unbiased and Low-variance Pseudo-labels is
  Critical for Semi-supervised Classification},
    author={Jiaqi Wu, Junbiao Pang, Baochang Zhang, Qingming Huang},
    journal={arXiv preprint arXiv:2403.18407},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.18407},
    primaryClass={cs.CV cs.AI}
}