HRN: A Holistic Approach to One Class Learning

By Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu. 2020

71 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning, Anomaly & Outlier Detection

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Representation & Self-Supervised Learning

1. Zixuan Ke
2. Qi Qin
3. Bin Liu
4. Wenpeng Hu
5. Tatsuya Konishi
6. Gyuhak Kim
7. Jinwen Ma
8. Changnan Xiao

In Anomaly & Outlier Detection

1. Bing Liu

Citations in the corpus

• A Theoretical Study on Solving Continual Learning (2022, background): “, by cross entropy) will result in poor OOD detection [78] because those missing features may be necessary to separate IND and some OOD data.”
• Recent Advances of Continual Learning in Computer Vision: An Overview (2021, neutral): “This is achieved by adapting the one-class loss with holistic regularization [35] in continual learning scenarios.”
• Continual Learning by Using Information of Each Class Holistically (2021, methodology): “PCL mainly uses the one-class classification method in (Hu et al. 2020).”
• Anomaly Detection via Reverse Distillation from One-Class Embedding (2022, methodology): “The baselines in this experiment include LSA [1], OCGAN [27], HRN [17], DAAD [16] and MKD [33].”
• CMG: A Class-Mixed Generation Approach to Out-of-Distribution Detection (2022, unknown): “So far, many approaches have been proposed to solve this problem, from distance-based meth-ods [2, 3, 12, 20], to generative models [60, 38, 41, 36] and self-supervised learning methods [4, 11, 17, 19].”