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].
$i$-Mix: A Domain-Agnostic Strategy for Contrastive Representation Learning
Authors: Kibok Lee, Yian Zhu, Kihyuk Sohn, Chun-Liang Li, Jinwoo Shin, Honglak Lee
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, we demonstrate that i-Mix consistently improves the quality of learned representations across domains, including image, speech, and tabular data. Furthermore, we confirm its regularization effect via extensive ablation studies across model and dataset sizes. |
| Researcher Affiliation | Collaboration | 1University of Michigan 2Amazon Web Services 3Google Cloud AI 4KAIST 5LG AI Research |
| Pseudocode | Yes | Algorithm 1 Loss computation for i-Mix on N-pair contrastive learning in Py Torch-like style. |
| Open Source Code | Yes | The code is available at https://github.com/kibok90/imix. |
| Open Datasets | Yes | CIFAR-10/100 (Krizhevsky & Hinton, 2009) consist of 50k training and 10k test images, and Image Net (Deng et al., 2009) has 1.3M training and 50k validation images... |
| Dataset Splits | Yes | CIFAR-10/100 (Krizhevsky & Hinton, 2009) consist of 50k training and 10k test images, and Image Net (Deng et al., 2009) has 1.3M training and 50k validation images... |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., specific GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments, only general model architectures like Res Net-50. |
| Software Dependencies | No | The paper mentions 'Py Torch-like style' for Algorithm 1 and adapting 'code for supervised contrastive learning', but does not provide specific software names with version numbers. |
| Experiment Setup | Yes | Models are trained with a batch size of 256 (i.e., 512 including augmented data) for up to 4000 epochs on CIFAR-10 and 100, and with a batch size of 512 for 800 epochs on Image Net. For i-Mix, we sample a mixing coefficient λ Beta(α, α) for each data, where α = 1 unless otherwise stated. The temperature is set to τ = 0.2. The memory bank size of Mo Co is 65536 for Image Net and 4096 for other datasets, and the momentum for the exponential moving average (EMA) update is 0.999 for Mo Co and BYOL. |