Robust Semi-Supervised Learning for Self-learning Open-World Classes
Wenjuan Xi Xin Song Weili Guo Yang Yang
Existing semi-supervised learning (SSL) methods assume that labeled and
unlabeled data share the same class space. However, in real-world applications,
unlabeled data always contain classes not present in the labeled set, which may
cause classification performance degradation of known classes. Therefore,
open-world SSL approaches are researched to handle the presence of multiple
unknown classes in the unlabeled data, which aims to accurately classify known
classes while fine-grained distinguishing different unknown classes. To address
this challenge, in this paper, we propose an open-world SSL method for
Self-learning Open-world Classes (SSOC), which can explicitly self-learn
multiple unknown classes. Specifically, SSOC first defines class center tokens
for both known and unknown classes and autonomously learns token
representations according to all samples with the cross-attention mechanism. To
effectively discover novel classes, SSOC further designs a pairwise similarity
loss in addition to the entropy loss, which can wisely exploit the information
available in unlabeled data from instances' predictions and relationships.
Extensive experiments demonstrate that SSOC outperforms the state-of-the-art
baselines on multiple popular classification benchmarks. Specifically, on the
ImageNet-100 dataset with a novel ratio of 90%, SSOC achieves a remarkable 22%
improvement.