HRN: A Holistic Approach to One Class Learning
Authors: Wenpeng Hu, Mengyu Wang, Qi Qin, Jinwen Ma, Bing Liu
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluation using both benchmark image classification and traditional anomaly detection datasets show that HRN markedly outperforms the state-of-the-art existing deep/non-deep learning models. |
| Researcher Affiliation | Academia | 1Department of Information Science, School of Mathematical Sciences, Peking University 2Wangxuan Institute of Computer Technology, Peking University 3Center for Data Science, AAIS, Peking University |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code of HRN can be found here3. https://github.com/morning-dews/HRN |
| Open Datasets | Yes | MNIST [47]6 is a handwritten digit classification dataset... http://yann.lecun.com/exdb/mnist/; f MNIST (fashion-MNIST) [84]7 consists of a training set... https://github.com/zalandoresearch/fashion-mnist; CIFAR-10 [44]8 is also an image classification dataset... https://www.cs.toronto.edu/ kriz/cifar.html; KDDCUP99 9... http://kdd.ics.uci.edu/databases/kddcup99; Thyroid 10 uses the version... http://archive.ics.uci.edu/ml; Arrhythmia 11 uses the data split... http://archive.ics.uci.edu/ml |
| Dataset Splits | Yes | MNIST [47]... 60,000 for training and 10,000 for testing.; f MNIST (fashion-MNIST) [84]... 60,000 for training and 10,000 for testing.; CIFAR-10 [44]... 50,000 for training and 10,000 for testing.; TQM [82] set 10% of the data as the validation set for each dataset. ... We followed the TQM approach and used grid search. However, we used only the MNIST data to search for hyper-parameter values and then applied the values to all 5 datasets. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used, such as specific GPU/CPU models or cloud resources. It only mentions 'Each experiment on a class takes less than 5 minutes.' |
| Software Dependencies | No | The paper mentions 'SGD with moment as the optimizer' and 'Re LU or Leaky-Re LU' as activation functions, but it does not specify version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | A MLP of size [784-100]-[100-1] is used for MNIST and f MNIST, of size 3*[1024-300]-[900-300]-[300-1] for CIFAR-10 and of size [125-100]-[100-1] for KDDCUP99, and of size [6-100]-[100-1] for Thyroid. We use SGD with moment as the optimizer. The learning rate is 0.1. ... We run HRN 100 epochs... for λ, from 0 to 1 with step 0.05 and for n, from 1 to 20 with step 1. ... we get λ = 0.1 and n = 12... which were applied to all datasets in all experiments without change. |