Semi-supervised Learning (SSL) has been proven vulnerable to
out-of-distribution (OOD) samples in realistic large-scale unsupervised
datasets due to over-confident pseudo-labeling OODs as in-distribution (ID). A
key underlying problem is class-wise latent space spreading from closed seen
space to open unseen space, and the bias is further magnified in SSL's
self-training loops. To close the ID distribution set so that OODs are better
rejected for safe SSL, we propose Prototype Fission(PF) to divide class-wise
latent spaces into compact sub-spaces by automatic fine-grained latent space
mining, driven by coarse-grained labels only. Specifically, we form multiple
unique learnable sub-class prototypes for each class, optimized towards both
diversity and consistency. The Diversity Modeling term encourages samples to be
clustered by one of the multiple sub-class prototypes, while the Consistency
Modeling term clusters all samples of the same class to a global prototype.
Instead of "opening set", i.e., modeling OOD distribution, Prototype Fission
"closes set" and makes it hard for OOD samples to fit in sub-class latent
space. Therefore, PF is compatible with existing methods for further
performance gains. Extensive experiments validate the effectiveness of our
method in open-set SSL settings in terms of successfully forming sub-classes,
discriminating OODs from IDs and improving overall accuracy. Codes will be
released.

通过原型分裂 (Prototype Fission，PF) 将类别潜空间分为紧凑的子空间，由粗粒度标签驱动自动细粒度潜空间挖掘，以关闭内部分布集，从而提高半监督学习中对 OOD 样本的拒绝率。