Fourier Self-Supervision for Fine-Grained Generalized Category Discovery

Generalized Category Discovery aims to recognize knowncategories while identifying novel ones within unlabeled data. Existingmethods, typically based on self-supervision and contrastive learning,often struggle to capture fine-grained distinctions, relying on superficialvisual cues rather than the intrinsic attributes humans use for categoriza-tion. We introduce Fourier Self-Supervision, that leverages the Fouriertransform of images to enhance the discrimination of subtle differencesand support the discovery of new categories. Our method employs adual frequency filtering strategy: a low-pass filter first extracts broad,abstract attributes that capture high-level category information, whilea high-pass filter emphasizes fine details such as edges and texturesthat are essential for fine-grained recognition. Each operates on a dedi-cated latent space, and their overlapping representations together yielda richer, more complete feature space. This dual-frequency approachnot only refines feature extraction to identify novel categories, but alsostrengthens the model’s discriminative power in fine-grained categorydiscovery. Experiments on multiple fine-grained datasets show that incor-porating Fourier Self-Supervision outperforms state-of-the-art methods,even when the number of classes is unknown, demonstrating its effec-tiveness for Generalized Category Discovery. Our code is available at:https://github.com/SarahRastegar/FourEx.