Spacing Loss for Discovering Novel Categories

• URL: http://arxiv.org/abs/2204.10595v1
• Date: Fri, 22 Apr 2022 09:37:11 GMT
• Title: Spacing Loss for Discovering Novel Categories
• Authors: K J Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth N Balasubramanian
• Abstract summary: Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data. 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. We devise a simple yet powerful loss function that enforces separability in the latent space using cues from multi-dimensional scaling.
• Score: 72.52222295216062
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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