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..
Learning Instance-Specific Augmentations by Capturing Local Invariances
Authors: Ning Miao, Tom Rainforth, Emile Mathieu, Yann Dubois, Yee Whye Teh, Adam Foster, Hyunjik Kim
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically demonstrate that Insta Aug learns meaningful input-dependent augmentations for a wide range of transformation classes, which in turn provides better performance on both supervised and self-supervised tasks. |
| Researcher Affiliation | Collaboration | 1Dept. of Statistics, University of Oxford 2Stanford University 3Microsoft Research 4Deep Mind, UK. |
| Pseudocode | Yes | Algorithm 1 Location related parameterization |
| Open Source Code | Yes | Accompanying code is provided at https://github.com/Ning Miao/Insta Aug. |
| Open Datasets | Yes | We first evaluate the performance of jointly training Insta Aug and the classifier on Tiny-Imagenet (Tiny IN, 64 64)... We exploit this on the larger Imagenet dataset (224 224) (Deng et al., 2009)... We benchmark on the texture classification dataset Raw Foo T (Bianco et al., 2017). |
| Dataset Splits | Yes | A scheduler is used to decrease the learning rate by a factor of 0.9 once validation accuracy doesn t increase for 10 epochs. ... To further investigate the effect of each augmentation method, we additionally split the 46 test sets into two equally-sized groups. |
| Hardware Specification | Yes | On a single 1080Ti, each iteration of training Insta Aug on Tiny IN takes 0.25s |
| Software Dependencies | No | The paper mentions using SGD and Adam optimizers, and refers to 'Mixmo codebase' and a codebase from Ermolov et al., 2021, which implies software frameworks like PyTorch. However, it does not specify exact version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | For the classifier, the initial learning rate is set to 0.2 (with momentum 0.9 and weight decay 1e 4). ... The learning rate of the augmentation module ϕ is fixed at 1e 5. Batch size is set to 100 and we pre-train Insta Aug for 10 epochs without augmentation. We train the model until convergence and the maximum epoch is set to 150. |