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..
AdaAug: Learning Class- and Instance-adaptive Data Augmentation Policies
Authors: Tsz-Him Cheung, Dit-Yan Yeung
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that the adaptive augmentation policies learned by our method transfer well to unseen datasets such as the Oxford Flowers, Oxford-IIT Pets, FGVC Aircraft, and Stanford Cars datasets when compared with other Auto DA baselines. In addition, our method also achieves a state-of-the-art performance on the CIFAR-10, CIFAR-100, and SVHN datasets.1 |
| Researcher Affiliation | Academia | Tsz-Him Cheung & Dit-Yan Yeung Department of Computer Science and Engineering The Hong Kong University of Science and Technology EMAIL |
| Pseudocode | Yes | Algorithm 1 Search algorithm |
| Open Source Code | Yes | Code is available at https://github.com/jamestszhim/adaptive_augment |
| Open Datasets | Yes | We search for the optimal augmentation policy on the CIFAR-100 dataset and use the learned policy to train with four fine-grained classification datasets: Oxford 102 Flowers (Nilsback & Zisserman, 2008), Oxford-IIIT Pets (Em et al., 2017), FGVC Aircraft (Maji et al., 2013), and Stanford Cars (Krause et al., 2013). We compare Ada Aug-direct with state-of-the-art Auto DA methods using the same evaluation datasets: CIFAR10, CIFAR-100 (Krizhevsky & Hinton, 2009), and SVHN (Netzer et al., 2011). |
| Dataset Splits | Yes | We follow the setup adopted by Auto Augment (Cubuk et al., 2019) to use 4,000 training images for CIFAR-10 and CIFAR-100, and 1,000 training images for SVHN. The remaining images are used as the validation set. |
| Hardware Specification | Yes | Ada Aug takes only 3.3 GPU hours on an old Ge Force GTX 1080 GPU card (see Appendix A.4). |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer' but does not specify version numbers for any software libraries, frameworks (e.g., PyTorch, TensorFlow), or programming languages used. |
| Experiment Setup | Yes | We implement h as a linear layer and update the policy parameter γ after every 10 training steps using the Adam optimizer with a learning rate of 0.001 and a batch size of 128. We use the cosine learning rate decay with one annealing cycle (Loshchilov & Hutter, 2017), initial learning rate of 0.1, weight decay 1e-4 and gradient clipping parameter 5. |