Vaibhav Rathore — accepted AI-conference papers

2025

HIDISC: A Hyperbolic Framework for Domain Generalization with Generalized Category Discovery

Vaibhav Rathore, Divyam Gupta, Biplab Banerjee

NeurIPS 2025poster

Generalized Category Discovery (GCD) aims to classify test-time samples into either seen categories—available during training—or novel ones, without relying on label supervision. Most existing GCD methods assume simultaneous access to labeled and unlabeled data during training and arising from the s…

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2025

When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach

Vaibhav Rathore, Shubhranil B, Saikat Dutta, Sarthak Mehrotra, Zsolt Kira, Biplab Banerjee

CVPR 2025poster

Generalized Class Discovery (GCD) clusters base and novel classes in a target domain, using supervision from a source domain with only base classes. Current methods often falter with distribution shifts and typically require access to target data during training, which can sometimes be impractical.…

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