# Open-World Class Discovery with Kernel Networks

arXiv: https://arxiv.org/abs/2012.06957 · HTML: https://mlsanity.com/paper/2012.06957/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 12 of 12)

- [Towards Discovery and Attribution of Open-world GAN Generated Images](https://mlsanity.com/paper/2105.04580/) (2021)
- [Novel Class Discovery: an Introduction and Key Concepts](https://mlsanity.com/paper/2302.12028/) (2023)
- [Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness](https://mlsanity.com/paper/2106.02734/) (2021)
- [Deep Learning on Multimodal Sensor Data at the Wireless Edge for Vehicular Network](https://mlsanity.com/paper/2201.04712/) (2022)
- [DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning](https://mlsanity.com/paper/2305.00380/) (2023)
- [A Method for Discovering Novel Classes in Tabular Data](https://mlsanity.com/paper/2209.01217/) (2022)
- [G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns](https://mlsanity.com/paper/2308.05463/) (2023)
- [NC-NCD: Novel Class Discovery for Node Classification](https://mlsanity.com/paper/2407.17816/) (2024)
- [Novel Categories Discovery Via Constraints on Empirical Prediction Statistics](https://mlsanity.com/paper/2307.03856/) (2023)
- [Novel Class Discovery for Point Cloud Segmentation via Joint Learning of Causal Representation and Reasoning](https://mlsanity.com/paper/2510.13307/) (2025)
- [Detecting Novelties with Empty Classes](https://mlsanity.com/paper/2305.00983/) (2023)
- [Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario](https://mlsanity.com/paper/2308.14119/) (2023)

## Abstract (arXiv)

We study an Open-World Class Discovery problem in which, given labeled training samples from old classes, we need to discover new classes from unlabeled test samples. There are two critical challenges to addressing this paradigm: (a) transferring knowledge from old to new classes, and (b) incorporating knowledge learned from new classes back to the original model. We propose Class Discovery Kernel Network with Expansion (CD-KNet-Exp), a deep learning framework, which utilizes the Hilbert Schmidt Independence Criterion to bridge supervised and unsupervised information together in a systematic way, such that the learned knowledge from old classes is distilled appropriately for discovering new classes. Compared to competing methods, CD-KNet-Exp shows superior performance on three publicly available benchmark datasets and a challenging real-world radio frequency fingerprinting dataset.