Fourier Self-Supervision for Fine-Grained Generalized Category Discovery

arXiv:2608.08963 · cs.CV, cs.AI, cs.LG · Submitted 2026-08-09 · Read on arXiv

Sarah Rastegar, Mina Ghadimi Atigh, Pascal Mettes, Yuki M. Asano, Cees G. M. Snoek

cs.CV, cs.AI, cs.LG

Submitted: 2026-08-09

Updated: 2026-08-11

Comments: Accepted by ECCV 2026

Code: https://github.com/SarahRastegar/FourEx

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Generalized Category Discovery — 40× in this paper
• Contrastive Learning — 20× in this paper · explained in 9 episodes
• Generalization — 20× in this paper · explained in 14 episodes
• Augmentation — 17× in this paper · explained in 2 episodes
• Robustness — 10× in this paper · explained in 6 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Semi-supervised Learning — 7× in this paper · explained in 4 episodes
• Clustering — 6× in this paper · explained in 2 episodes
• Domain Adaptation — 5× in this paper · explained in 5 episodes
• Generalizability — 5× in this paper · explained in 6 episodes
• Overfitting — 4× in this paper · explained in 2 episodes
• Pseudo-labels — 4× in this paper · explained in 2 episodes
• Signal-to-Noise Ratio (SNR) — 4× in this paper · explained in 6 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Data Augmentation — 3× in this paper · explained in 10 episodes
• Fine-Tuning — 3× in this paper · explained in 5 episodes
• Fourier Domain Adaptation — 3× in this paper
• Hierarchical Structure — 3× in this paper · explained in 3 episodes
• Latent Space — 3× in this paper · explained in 7 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Representation Learning — 3× in this paper · explained in 3 episodes
• Self-Supervised Learning — 3× in this paper · explained in 8 episodes
• Class Imbalance — 2× in this paper · explained in 3 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Fast Fourier Transform — 2× in this paper
• Feature Extraction — 2× in this paper · explained in 3 episodes
• Unsupervised Learning — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Abstraction — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 4 episodes
• Catastrophic Forgetting — 1× in this paper · explained in 9 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cosine Similarity — 1× in this paper · explained in 3 episodes
• Distillation — 1× in this paper · explained in 2 episodes
• Hyperparameters — 1× in this paper · explained in 2 episodes
• K-means Clustering — 1× in this paper · explained in 3 episodes
• Knowledge Distillation — 1× in this paper · explained in 15 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Semantic Segmentation — 1× in this paper · explained in 3 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformers — 1× in this paper · explained in 2 episodes

Sources

• Learning to cluster in order to transfer across domains and tasks
• Multi-class Classification without Multi-class Labels
• Generalized Category Discovery via Reciprocal Learning and Class-Wise Distribution Regularization
• Fine-Grained Visual Classification of Aircraft
• An Overview of Deep Semi-Supervised Learning
• The Herbarium Challenge 2019 Dataset

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