Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Adversarial Learning for Robust Deep Clustering
Authors: Xu Yang, Cheng Deng, Kun Wei, Junchi Yan, Wei Liu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on two popular datasets show that the proposed adversarial learning method can significantly enhance the robustness and further improve the overall clustering performance. |
| Researcher Affiliation | Collaboration | 1School of Electronic Engineering, Xidian University, Xian 710071, China 2Department of CSE and Mo E Key Lab of Artificial Intelligence, Shanghai Jiao Tong University 3Tencent AI Lab, Shenzhen, China |
| Pseudocode | Yes | Algorithm 1 Adversarial Learning for Robust Deep Clustering |
| Open Source Code | Yes | The source code is available at https://github.com/xdxuyang/ALRDC. |
| Open Datasets | Yes | MNIST [18]: containing a total of 70,000 handwritten digits with 60,000 training and 10,000 testing samples, each being a 28 × 28 monochrome image. Fashion MNIST [31]: having the same number of images with the same image size as MNIST, but fairly more complicated. |
| Dataset Splits | Yes | MNIST [18]: containing a total of 70,000 handwritten digits with 60,000 training and 10,000 testing samples, each being a 28 × 28 monochrome image. Fashion MNIST [31]: having the same number of images with the same image size as MNIST, but fairly more complicated. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU types, or memory. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies (e.g., Python, PyTorch, TensorFlow, or other libraries). |
| Experiment Setup | Yes | In our experiments, we set λ = 1. The hyper-parameters β and γ are determined by different networks and datasets... For MNIST, the channel numbers and kernel sizes of the autoencoder network are the same as those in [37], and we employ one convolutional layer and three following residual blocks in the encoder for Fashion-MNIST. The clustering layers consist of four fully-connected layers, and Re LU is employed as nonlinear activation. |