Spacing Loss for Discovering Novel Categories
By K. J. Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, Vineeth Nallure Balasubramanian. 2022
18 citations recorded by the source corpus.
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 singlestage 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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- K. J. Joseph
- Vineeth Nallure Balasubramanian
- Piyush Rai
- Soma Biswas
- Sujoy Paul
- Gaurav Aggarwal
- Kai Han
- Jun Zhu
Citations in the corpus
- Novel Class Discovery without Forgetting (2022, methodology): “[28] uses cues from multi-dimensional scaling to enforce latent space separability, while Jia et al.”