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..
MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps
Authors: Awais Muhammad, Fengwei Zhou, Chuanlong Xie, Jiawei Li, Sung-Ho Bae, Zhenguo Li
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, extensive experiments on multiple datasets and different learning scenarios show our method can transfer robustness while also improving generalization on natural images. |
| Researcher Affiliation | Collaboration | 1 Huawei Noah s Ark Lab 2 Department of Computer Science, Kyung-Hee University, South Korea |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The webpage for the project is available at: awaisrauf.github.io/Mix ACM. This is a project webpage, not a direct link to a source code repository. |
| Open Datasets | Yes | We have conducted extensive experiments to show the effectiveness of our method using various datasets and under different learning settings. ... on CIFAR-10, CIFAR100, and Image Net datasets. |
| Dataset Splits | No | The paper mentions using CIFAR-10, CIFAR-100, and ImageNet datasets but does not explicitly describe the training, validation, and test splits used for these datasets or provide citations for predefined splits beyond using 'test accuracy' for evaluation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch implementation' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For CIFAR experiments, ... We trained them for 200 epochs, using batch size of 128, a learning rate of 0.1, cosine learning rate scheduler [46], momentum optimizer with weight decay of 0.0005. For our loss, we use αacm = 5000. For KD loss, we use temperature value of γ = 10 and αkld = 0.95 and the value for mixup coefficient is αmixup = 1 whereas λ Beta(αmixup, αmixup) following [95]. Image Net models are trained for 120 epochs. |