Learning with Mixture of Prototypes for Out-of-Distribution Detection
Haodong Lu Dong Gong Shuo Wang Jason Xue Lina Yao Kristen Moore
Out-of-distribution (OOD) detection aims to detect testing samples far away
from the in-distribution (ID) training data, which is crucial for the safe
deployment of machine learning models in the real world. Distance-based OOD
detection methods have emerged with enhanced deep representation learning. They
identify unseen OOD samples by measuring their distances from ID class
centroids or prototypes. However, existing approaches learn the representation
relying on oversimplified data assumptions, e.g, modeling ID data of each class
with one centroid class prototype or using loss functions not designed for OOD
detection, which overlook the natural diversities within the data. Naively
enforcing data samples of each class to be compact around only one prototype
leads to inadequate modeling of realistic data and limited performance. To
tackle these issues, we propose PrototypicAl Learning with a Mixture of
prototypes (PALM) which models each class with multiple prototypes to capture
the sample diversities, and learns more faithful and compact samples embeddings
to enhance OOD detection. Our method automatically identifies and dynamically
updates prototypes, assigning each sample to a subset of prototypes via
reciprocal neighbor soft assignment weights. PALM optimizes a maximum
likelihood estimation (MLE) loss to encourage the sample embeddings to be
compact around the associated prototypes, as well as a contrastive loss on all
prototypes to enhance intra-class compactness and inter-class discrimination at
the prototype level. Moreover, the automatic estimation of prototypes enables
our approach to be extended to the challenging OOD detection task with
unlabelled ID data. Extensive experiments demonstrate the superiority of PALM,
achieving state-of-the-art average AUROC performance of 93.82 on the
challenging CIFAR-100 benchmark. Code is available at
https://github.com/jeff024/PALM.