Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances from a disjoint set of classes. In this work, we first characterize existing NCD approaches into single-stage and two-stage methods based on whether they require access to labeled and unlabeled data together while discovering new classes. Next, we devise a simple yet powerful loss function that enforces separability in the latent space using cues from multi-dimensional scaling, which we refer to as Spacing Loss. Our proposed formulation can either operate as a standalone method or can be plugged into existing methods to enhance them. We validate the efficacy of Spacing Loss with thorough experimental evaluation across multiple settings on CIFAR-10 and CIFAR-100 datasets.
@article{arxiv.2204.10595,
title = {Spacing Loss for Discovering Novel Categories},
author = {K J Joseph and Sujoy Paul and Gaurav Aggarwal and Soma Biswas and Piyush Rai and Kai Han and Vineeth N Balasubramanian},
journal= {arXiv preprint arXiv:2204.10595},
year = {2022}
}
Comments
Accepted to Continual Learning in Computer Vision Workshop (CLVision) at CVPR 2022