Self-supervised Label Augmentation via Input Transformations
Authors: Hankook Lee, Sung Ju Hwang, Jinwoo Shin
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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. |