# Deep Transfer Learning for Multiple Class Novelty Detection

arXiv: https://arxiv.org/abs/1903.02196 · HTML: https://mlsanity.com/paper/1903.02196/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2019.

## Built on by (most cited first, 30 of 33)

- [Generalized Out-of-Distribution Detection: A Survey](https://mlsanity.com/paper/2110.11334/) (2021)
- [Open-Set Recognition: a Good Closed-Set Classifier is All You Need?](https://mlsanity.com/paper/2110.06207/) (2021)
- [OpenOOD v1.5: Enhanced Benchmark for Out-of-Distribution Detection](https://mlsanity.com/paper/2306.09301/) (2023)
- [A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future Challenges](https://mlsanity.com/paper/2110.14051/) (2021)
- [OpenGAN: Open-Set Recognition via Open Data Generation](https://mlsanity.com/paper/2104.02939/) (2021)
- [Mixture Outlier Exposure: Towards Out-of-Distribution Detection in Fine-grained Environments](https://mlsanity.com/paper/2106.03917/) (2021)
- [A Review of Deep Transfer Learning and Recent Advancements](https://mlsanity.com/paper/2201.09679/) (2022)
- [The Eyecandies Dataset for Unsupervised Multimodal Anomaly Detection and Localization](https://mlsanity.com/paper/2210.04570/) (2022)
- [One-Class Classification: A Survey](https://mlsanity.com/paper/2101.03064/) (2021)
- [OpenAUC: Towards AUC-Oriented Open-Set Recognition](https://mlsanity.com/paper/2210.13458/) (2022)
- [Towards Open Set Video Anomaly Detection](https://mlsanity.com/paper/2208.11113/) (2022)
- [Adversarially Robust One-class Novelty Detection](https://mlsanity.com/paper/2108.11168/) (2021)
- [Open-set Adversarial Defense with Clean-Adversarial Mutual Learning](https://mlsanity.com/paper/2202.05953/) (2022)
- [Ultralytics YOLO Evolution: An Overview of YOLO27, YOLO26, YOLO11, YOLOv8, and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition](https://mlsanity.com/paper/2510.09653/) (2025)
- [Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?](https://mlsanity.com/paper/2105.09270/) (2021)
- [Benchmarking Safety Monitors for Image Classifiers with Machine Learning](https://mlsanity.com/paper/2110.01232/) (2021)
- [Visual Anomaly Detection for Images: A Survey](https://mlsanity.com/paper/2109.13157/) (2021)
- [LMC: Large Model Collaboration with Cross-assessment for Training-Free Open-Set Object Recognition](https://mlsanity.com/paper/2309.12780/) (2023)
- [Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey](https://mlsanity.com/paper/2203.00190/) (2022)
- [Multi-Class Anomaly Detection](https://mlsanity.com/paper/2110.15108/) (2021)
- [All Beings Are Equal in Open Set Recognition](https://mlsanity.com/paper/2401.17654/) (2024)
- [BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors](https://mlsanity.com/paper/2503.17717/) (2025)
- [A Data-Driven Novelty Score for Diverse In-Vehicle Data Recording](https://mlsanity.com/paper/2507.04529/) (2025)
- [Dealing with Distribution Mismatch in Semi-supervised Deep Learning for Covid-19 Detection Using Chest X-ray Images: A Novel Approach Using Feature Densities](https://mlsanity.com/paper/2109.00889/) (2021)
- [Reconstructing Humpty Dumpty: Multi-feature Graph Autoencoder for Open Set Action Recognition](https://mlsanity.com/paper/2212.06023/) (2022)
- [torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in Python](https://mlsanity.com/paper/2305.09646/) (2023)
- [LORD: Leveraging Open-Set Recognition with Unknown Data](https://mlsanity.com/paper/2308.12584/) (2023)
- [Latent Space Energy-based Model for Fine-grained Open Set Recognition](https://mlsanity.com/paper/2309.10711/) (2023)
- [Adaptive Label Smoothing for Out-of-Distribution Detection](https://mlsanity.com/paper/2410.06134/) (2024)
- [Improving Novelty Detection using the Reconstructions of Nearest Neighbours](https://mlsanity.com/paper/2111.06150/) (2021)

## Abstract (arXiv)

We propose a transfer learning-based solution for the problem of multiple class novelty detection. In particular, we propose an end-to-end deep-learning based approach in which we investigate how the knowledge contained in an external, out-of-distributional dataset can be used to improve the performance of a deep network for visual novelty detection. Our solution differs from the standard deep classification networks on two accounts. First, we use a novel loss function, membership loss, in addition to the classical cross-entropy loss for training networks. Secondly, we use the knowledge from the external dataset more effectively to learn globally negative filters, filters that respond to generic objects outside the known class set. We show that thresholding the maximal activation of the proposed network can be used to identify novel objects effectively. Extensive experiments on four publicly available novelty detection datasets show that the proposed method achieves significant improvements over the state-of-the-art methods.