# Modeling Inter-Class and Intra-Class Constraints in Novel Class Discovery

arXiv: https://arxiv.org/abs/2210.03591 · HTML: https://mlsanity.com/paper/2210.03591/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 7 Oct 2022.

## Builds on (most cited first, 26 of 26)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Unsupervised Learning of Visual Features by Contrasting Cluster Assignments](https://mlsanity.com/paper/2006.09882/) (2020)
- [Sinkhorn Distances: Lightspeed Computation of Optimal Transportation Distances](https://mlsanity.com/paper/1306.0895/) (2013)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Unsupervised Deep Embedding for Clustering Analysis](https://mlsanity.com/paper/1511.06335/) (2015)
- [Invariant Information Clustering for Unsupervised Image Classification and Segmentation](https://mlsanity.com/paper/1807.06653/) (2018)
- [Learning to Discover Novel Visual Categories via Deep Transfer Clustering](https://mlsanity.com/paper/1908.09884/) (2019)
- [Generalized Category Discovery](https://mlsanity.com/paper/2201.02609/) (2022)
- [A Unified Objective for Novel Class Discovery](https://mlsanity.com/paper/2108.08536/) (2021)
- [Automatically Discovering and Learning New Visual Categories with Ranking Statistics](https://mlsanity.com/paper/2002.05714/) (2020)
- [Learning to cluster in order to transfer across domains and tasks](https://mlsanity.com/paper/1711.10125/) (2017)
- [Neighborhood Contrastive Learning for Novel Class Discovery](https://mlsanity.com/paper/2106.10731/) (2021)
- [SimMatch: Semi-supervised Learning with Similarity Matching](https://mlsanity.com/paper/2203.06915/) (2022)
- [Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation](https://mlsanity.com/paper/2107.03358/) (2021)
- [Multi-class Classification without Multi-class Labels](https://mlsanity.com/paper/1901.00544/) (2019)
- [OpenMix: Reviving Known Knowledge for Discovering Novel Visual Categories in An Open World](https://mlsanity.com/paper/2004.05551/) (2020)
- [Joint Representation Learning and Novel Category Discovery on Single- and Multi-modal Data](https://mlsanity.com/paper/2104.12673/) (2021)
- [Spacing Loss for Discovering Novel Categories](https://mlsanity.com/paper/2204.10595/) (2022)

## Built on by (most cited first, 26 of 26)

- [Learn to Categorize or Categorize to Learn? Self-Coding for Generalized Category Discovery](https://mlsanity.com/paper/2310.19776/) (2023)
- [Solving the Catastrophic Forgetting Problem in Generalized Category Discovery](https://mlsanity.com/paper/2501.05272/) (2025)
- [ProtoGCD: Unified and Unbiased Prototype Learning for Generalized Category Discovery](https://mlsanity.com/paper/2504.03755/) (2025)
- [Happy: A Debiased Learning Framework for Continual Generalized Category Discovery](https://mlsanity.com/paper/2410.06535/) (2024)
- [Novel Class Discovery for Ultra-Fine-Grained Visual Categorization](https://mlsanity.com/paper/2405.06283/) (2024)
- [Seeing Unseen: Discover Novel Biomedical Concepts via Geometry-Constrained Probabilistic Modeling](https://mlsanity.com/paper/2403.01053/) (2024)
- [Flipped Classroom: Aligning Teacher Attention with Student in Generalized Category Discovery](https://mlsanity.com/paper/2409.19659/) (2024)
- [Category Discovery: An Open-World Perspective](https://mlsanity.com/paper/2509.22542/) (2025)
- [Open-World Deepfake Attribution via Confidence-Aware Asymmetric Learning](https://mlsanity.com/paper/2512.12667/) (2025)
- [Generalized Category Discovery in Semantic Segmentation](https://mlsanity.com/paper/2311.11525/) (2023)
- [Federated Continual Novel Class Learning](https://mlsanity.com/paper/2312.13500/) (2023)
- [Learning Part Knowledge to Facilitate Category Understanding for Fine-Grained Generalized Category Discovery](https://mlsanity.com/paper/2503.16782/) (2025)
- [Generalized Category Discovery under Domain Shift: A Frequency Domain Perspective](https://mlsanity.com/paper/2511.00573/) (2025)
- [Self-Cooperation Knowledge Distillation for Novel Class Discovery](https://mlsanity.com/paper/2407.01930/) (2024)
- [CSPCL: Category Semantic Prior Contrastive Learning for Deformable DETR-Based Prohibited Item Detectors](https://mlsanity.com/paper/2501.16665/) (2025)
- [Continual Novel Class Discovery via Feature Enhancement and Adaptation](https://mlsanity.com/paper/2405.06389/) (2024)
- [MMCL: Correcting Content Query Distributions for Improved Anti-Overlapping X-Ray Object Detection](https://mlsanity.com/paper/2406.03176/) (2024)
- [Generalized Fine-Grained Category Discovery with Multi-Granularity Conceptual Experts](https://mlsanity.com/paper/2509.26227/) (2025)
- [From Uncertainty to Clarity: Uncertainty-Guided Class-Incremental Learning for Limited Biomedical Samples via Semantic Expansion](https://mlsanity.com/paper/2409.07757/) (2024)
- [Affinity-Graph-Guided Contractive Learning for Pretext-Free Medical Image Segmentation with Minimal Annotation](https://mlsanity.com/paper/2410.10366/) (2024)
- [Sharpness-aware Dynamic Anchor Selection for Generalized Category Discovery](https://mlsanity.com/paper/2512.12925/) (2025)
- [TALON: Test-time Adaptive Learning for On-the-Fly Category Discovery](https://mlsanity.com/paper/2603.08075/) (2026)
- [Intra-view and Inter-view Correlation Guided Multi-view Novel Class Discovery](https://mlsanity.com/paper/2507.12029/) (2025)
- [ViRN: Variational Inference and Distribution Trilateration for Long-Tailed Continual Representation Learning](https://mlsanity.com/paper/2507.17368/) (2025)
- [The Devil Is in Gradient Entanglement: Energy-Aware Gradient Coordinator for Robust Generalized Category Discovery](https://mlsanity.com/paper/2604.14176/) (2026)
- [PHOEBI: An Open-World Benchmark for Multi-Label Bacterial Identification in Phase-Contrast Microscopy](https://mlsanity.com/paper/2606.22890/) (2026)

## Abstract (arXiv)

Novel class discovery (NCD) aims at learning a model that transfers the common knowledge from a class-disjoint labelled dataset to another unlabelled dataset and discovers new classes (clusters) within it. Many methods, as well as elaborate training pipelines and appropriate objectives, have been proposed and considerably boosted performance on NCD tasks. Despite all this, we find that the existing methods do not sufficiently take advantage of the essence of the NCD setting. To this end, in this paper, we propose to model both inter-class and intra-class constraints in NCD based on the symmetric Kullback-Leibler divergence (sKLD). Specifically, we propose an inter-class sKLD constraint to effectively exploit the disjoint relationship between labelled and unlabelled classes, enforcing the separability for different classes in the embedding space. In addition, we present an intra-class sKLD constraint to explicitly constrain the intra-relationship between a sample and its augmentations and ensure the stability of the training process at the same time. We conduct extensive experiments on the popular CIFAR10, CIFAR100 and ImageNet benchmarks and successfully demonstrate that our method can establish a new state of the art and can achieve significant performance improvements, e.g., 3.5%/3.7% clustering accuracy improvements on CIFAR100-50 dataset split under the task-aware/-agnostic evaluation protocol, over previous state-of-the-art methods. Code is available at https://github.com/FanZhichen/NCD-IIC.