Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
Authors: Kaiwen Yang, Yanchao Sun, Jiahao Su, Fengxiang He, Xinmei Tian, Furong Huang, Tianyi Zhou, Dacheng Tao
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we apply LP-A3 as a data augmentation method to several popular methods for three different learning tasks, i.e., (1) semi-supervised classification; (2) noisy-label learning and (3) medical image classification. In all the experiments, LP-A3 can (1) consistently improve the convergence and test accuracy of existing methods and (2) autonomously produce augmentations that bring non-trivial improvement even without any domain knowledge available. ... Semi-supervised learning To evaluate how LP-A3 improves the learning without sufficient labeled data, we conduct experiments on semi-supervised classification on standard benchmarks including CIFAR [22] and STL-10 [10] where only a very small amount of labels are revealed. ... We report the results in Table 3, where Rand Augment designed for natural images fails to improve the performance in this scenario. In contrast, LP-A3 does not rely on any domain knowledge brings improvement to all the datasets, especially for Oct MNIST where the improvement is over 1%. |
| Researcher Affiliation | Collaboration | University of Science and Technology of China1; University of Maryland, College Park2 Institute of Artificial Intelligence, Hefei Comprehensive National Science Center3 JD Explore Academy4; The University of Sydney5 |
| Pseudocode | Yes | The pseudo-code of plugging LP-A3 into the representation learning procedure with TCS-based data selection is provided in Algorithm 1. |
| Open Source Code | Yes | Code is available at: https://github.com/kai-wen-yang/LPA3. |
| Open Datasets | Yes | We conduct experiments on semi-supervised classification on standard benchmarks including CIFAR [22] and STL-10 [10]... medical image classification tasks from Med MNIST [50] |
| Dataset Splits | Yes | More experimental details can be found in the Appendix. ... Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Aappendix C |
| Hardware Specification | No | The paper states in its checklist that it did not include the total amount of compute and the type of resources used. While it mentions 'GPU cluster built by MCC Lab', it does not specify any particular hardware details such as GPU models or CPU types. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | More experimental details can be found in the Appendix. ... Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] See Aappendix C |