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..
Self-supervised Label Augmentation via Input Transformations
Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experimentally validate our self-supervised label augmentation techniques described in Section 2. Throughout this section, we refer to data augmentation LDA (2) as DA, multi-task learning LMT (1) as MT, and our self-supervised label augmentation LSLA (3) as SLA for notational simplicity. |
| Researcher Affiliation | Collaboration | 1School of Electrical Engineering, KAIST, Daejeon, Korea 2Graduate School of AI, KAIST, Daejeon, Korea 3School of Computing, KAIST, Daejeon, Korea 4AITRICS, Seoul, Korea. |
| Pseudocode | Yes | In the supplementary material, we provide pseudo-codes of our algorithm, which can be easily implemented. |
| Open Source Code | Yes | Code available at https://github.com/hankook/SLA. |
| Open Datasets | Yes | We evaluate our method on various classification datasets: CIFAR10/100 (Krizhevsky et al., 2009), Caltech-UCSD Birds or CUB200 (Wah et al., 2011), Indoor Scene Recognition or MIT67 (Quattoni & Torralba, 2009), Stanford Dogs (Khosla et al., 2011), and tiny-Image Net3 for standard or imbalanced image classification; mini-Image Net (Vinyals et al., 2016), CIFAR-FS (Bertinetto et al., 2019), and FC100 (Oreshkin et al., 2018) for few-shot classification. |
| Dataset Splits | No | The paper describes training datasets and test sets, but does not explicitly provide details about validation dataset splits (e.g., percentages, sample counts, or specific files/citations for validation sets). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions various methods and models (e.g., SGD, ResNet-32, Meta Opt Net) but does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions, or framework versions). |
| Experiment Setup | Yes | For the standard image classification datasets, we use SGD with a learning rate of 0.1, momentum of 0.9, and weight decay of 10 4. We train for 80k iterations with a batch size of 128. For the finegrained datasets, we train for 30k iterations with a batch size of 32 because they have a relatively smaller number of training samples. We decay the learning rate by the constant factor of 0.1 at 50% and 75% iterations. |