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 | Conference PDF | Archive PDF | Plain Text | 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. |