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 [1].
A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective
Authors: Chanwoo Park, Sangdoo Yun, Sanghyuk Chun
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical study shows that our HMix and GMix outperform the previous state-of-the-art MSDA methods in CIFAR-100 and Image Net classification tasks. |
| Researcher Affiliation | Collaboration | Chanwoo Park , MIT EMAIL Sangdoo Yun NAVER AI Lab EMAIL Sanghyuk Chun NAVER AI Lab EMAIL |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Source code is available at https://github.com/naver-ai/hmix-gmix. |
| Open Datasets | Yes | Our empirical study shows that our HMix and GMix outperform the previous state-of-the-art MSDA methods in CIFAR-100 and Image Net classification tasks. ... Results on CIFAR-100 classification. ... Results on Image Net-1K classification. |
| Dataset Splits | No | The paper mentions evaluating on a "validation dataset" for Figure 5, but does not provide explicit split percentages or sizes for all datasets used in the experiments in the main text. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types) used to run its experiments in the main text. |
| Software Dependencies | No | The paper mentions software like PyTorch Image Models but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We train networks for 300 epochs using SGD optimizer with a learning rate 0.2. ... We set the hyper-parameter α for Mixup, Cut Mix, and Stochastic Mixup & Cut Mix to 1. α for HMix and GMix were set to 1 and 0.5, respectively. We use r = 0.5 for HMix. ... We train Res Net50 [26] with various MSDA methods for 300 epochs using SGD optimizer with a learning rate 0.1. We set the hyper-parameter α for all methods except Mixup to 1, while Mixup has α = 0.8. We use r = 0.75 for HMix. |