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..
Class-Disentanglement and Applications in Adversarial Detection and Defense
Authors: Kaiwen Yang, Tianyi Zhou, Yonggang Zhang, Xinmei Tian, Dacheng Tao
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, this simple approach substantially improves the detection and defense against different types of adversarial attacks. |
| Researcher Affiliation | Collaboration | University of Science and Technology of China1; University of Washington, Seattle2 University of Maryland, College Park3; JD Explore Academy4 |
| Pseudocode | No | The paper includes an architecture diagram (Figure 1) and mathematical equations for the objective function and attacks, but no structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available: https://github.com/kai-wen-yang/CD-VAE. |
| Open Datasets | Yes | all the experiments are conducted on two datasets, CIFAR-10 [26] and restricted Image Net [44]. |
| Dataset Splits | No | The paper provides training and test set sizes ('50,000 training images and 10000 test images' for CIFAR-10, and '257,748 training images and 10,150 test images' for restricted Image Net) but does not specify details for a separate validation split. |
| Hardware Specification | No | The paper mentions 'GPU cluster built by MCC Lab of Information Science and Technology Institution, USTC' but does not provide specific GPU models, CPU details, or other hardware specifications. |
| Software Dependencies | No | The paper mentions 'Adam W' as the optimizer but does not specify versions for software libraries, frameworks, or other dependencies like Python, PyTorch, or TensorFlow. |
| Experiment Setup | Yes | For CIFAR-10, we use a VAE G( ) with a few convolutional layers [25] and Wide Res Net-28-10 [48] as the image classifier D( ). For restricted Image Net, due to the high resolution (299 299), we use VQ-VAE [38] as G( ) and Res Net-50 [19] as D( ). We set β = 0.2. During training, we use Adam W [12] with a weight decay of 1e-6 as the optimizer to minimize Eq. (1) in an end-to-end manner for 300(60) epochs on CIFAR-10(restricted Image Net). |