# Fine-grained Angular Contrastive Learning with Coarse Labels

arXiv: https://arxiv.org/abs/2012.03515 · HTML: https://mlsanity.com/paper/2012.03515/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 30 of 30)

- [A Survey on Self-supervised Learning: Algorithms, Applications, and Future Trends](https://mlsanity.com/paper/2301.05712/) (2023)
- [Dual Contrastive Learning for General Face Forgery Detection](https://mlsanity.com/paper/2112.13522/) (2021)
- [Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning](https://mlsanity.com/paper/2204.07596/) (2022)
- [Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views](https://mlsanity.com/paper/2206.00227/) (2022)
- [Unsupervised Domain Generalization by Learning a Bridge Across Domains](https://mlsanity.com/paper/2112.02300/) (2021)
- [A Practical Contrastive Learning Framework for Single-Image Super-Resolution](https://mlsanity.com/paper/2111.13924/) (2021)
- [Language-driven Grasp Detection](https://mlsanity.com/paper/2406.09489/) (2024)
- [Efficient Algorithms for Learning from Coarse Labels](https://mlsanity.com/paper/2108.09805/) (2021)
- [MaskCon: Masked Contrastive Learning for Coarse-Labelled Dataset](https://mlsanity.com/paper/2303.12756/) (2023)
- [Channel Importance Matters in Few-Shot Image Classification](https://mlsanity.com/paper/2206.08126/) (2022)
- [Fine-Grained Classification with Noisy Labels](https://mlsanity.com/paper/2303.02404/) (2023)
- [On the Importance of Distractors for Few-Shot Classification](https://mlsanity.com/paper/2109.09883/) (2021)
- [Fine-grained Category Discovery under Coarse-grained supervision with Hierarchical Weighted Self-contrastive Learning](https://mlsanity.com/paper/2210.07733/) (2022)
- [Semi-Supervised Learning with Taxonomic Labels](https://mlsanity.com/paper/2111.11595/) (2021)
- [From Channel Bias to Feature Redundancy: Uncovering the "Less is More" Principle in Few-Shot Learning](https://mlsanity.com/paper/2310.03843/) (2023)
- [Coarse-To-Fine Incremental Few-Shot Learning](https://mlsanity.com/paper/2111.14806/) (2021)
- [Hyperbolic Space with Hierarchical Margin Boosts Fine-Grained Learning from Coarse Labels](https://mlsanity.com/paper/2311.11019/) (2023)
- [Dual Contrastive Transformer for Hierarchical Preference Modeling in Sequential Recommendation](https://mlsanity.com/paper/2410.22790/) (2024)
- [Weakly Supervised Temporal Convolutional Networks for Fine-grained Surgical Activity Recognition](https://mlsanity.com/paper/2302.10834/) (2023)
- [A Generic Method for Fine-grained Category Discovery in Natural Language Texts](https://mlsanity.com/paper/2406.13103/) (2024)
- [DNA: Denoised Neighborhood Aggregation for Fine-grained Category Discovery](https://mlsanity.com/paper/2310.10151/) (2023)
- [Encoding Hierarchical Information in Neural Networks helps in Subpopulation Shift](https://mlsanity.com/paper/2112.10844/) (2021)
- [Tripartite: Tackle Noisy Labels by a More Precise Partition](https://mlsanity.com/paper/2202.09579/) (2022)
- [Dynamic Self-adaptive Multiscale Distillation from Pre-trained Multimodal Large Model for Efficient Cross-modal Representation Learning](https://mlsanity.com/paper/2404.10838/) (2024)
- [Fine-grained Classes and How to Find Them](https://mlsanity.com/paper/2406.11070/) (2024)
- [Twofold Debiasing Enhances Fine-Grained Learning with Coarse Labels](https://mlsanity.com/paper/2502.19816/) (2025)
- [ShufaNet: Classification method for calligraphers who have reached the professional level](https://mlsanity.com/paper/2111.11350/) (2021)
- [Class-Specific Channel Attention for Few-Shot Learning](https://mlsanity.com/paper/2209.01332/) (2022)
- [Efficient Subclass Segmentation in Medical Images](https://mlsanity.com/paper/2307.00257/) (2023)
- [Hyperbolic Coarse-to-Fine Few-Shot Class-Incremental Learning](https://mlsanity.com/paper/2509.18504/) (2025)

## Abstract (arXiv)

Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This adaptivity to unseen classes is especially important for many practical applications where the pre-trained label space cannot remain fixed for effective use and the model needs to be "specialized" to support new categories on the fly. One particularly interesting scenario, essentially overlooked by the few-shot literature, is Coarse-to-Fine Few-Shot (C2FS), where the training classes (e.g. animals) are of much `coarser granularity' than the target (test) classes (e.g. breeds). A very practical example of C2FS is when the target classes are sub-classes of the training classes. Intuitively, it is especially challenging as (both regular and few-shot) supervised pre-training tends to learn to ignore intra-class variability which is essential for separating sub-classes. In this paper, we introduce a novel 'Angular normalization' module that allows to effectively combine supervised and self-supervised contrastive pre-training to approach the proposed C2FS task, demonstrating significant gains in a broad study over multiple baselines and datasets. We hope that this work will help to pave the way for future research on this new, challenging, and very practical topic of C2FS classification.