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 Learning with an Information Maximization Criterion
Authors: Serdar Ozsoy, Shadi Hamdan, Sercan Arik, Deniz Yuret, Alper Erdogan
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical experiments demonstrate that Cor Info Max achieves better or competitive performance results relative to the state-of-the-art SSL approaches. Section 5 provides the numerical experiments illustrating the performance of the proposed approach. |
| Researcher Affiliation | Collaboration | 1KUIS AI Center, Koc University, Turkey 2EEE Department, Koc University, Turkey 3CE Department, Koc University, Turkey 4Google Cloud AI Research, Sunnyvale, CA |
| Pseudocode | Yes | (See Appendix C for Cor Info Max pseudocode.) |
| Open Source Code | Yes | Cor Info Max s source code is publicly available in https://github.com/serdarozsoy/ corinfomax-ssl |
| Open Datasets | Yes | Datasets: We perform experiments on CIFAR-10, CIFAR-100 [40], Tiny Image Net [41], COCO [42], Image Net-100 and Image Net-1K [43] datasets1. |
| Dataset Splits | Yes | Then, we obtain the test accuracy results for the trained linear classi๏ฌer based on the validation dataset. |
| Hardware Specification | Yes | For Image Net-100 and Image Net-1K, we pretrain our model on up to 8 A100 Cloud GPUs. The remaining datasets are trained using a single T4 and V100 Cloud GPU. |
| Software Dependencies | No | The paper mentions software components implicitly through the context of deep learning (e.g., training models), but does not provide specific version numbers for any libraries, frameworks, or languages used. |
| Experiment Setup | Yes | For pretraining, we use 1000 epochs with a batch size of 512 for CIFAR datasets, and 800 epochs with a batch size of 1024 for Tiny Image Net. [...] We use the SGD optimizer with a momentum of 0.9 and a weight decay of 1 10 4. The initial learning rate is 0.5 for CIFAR datasets and Tiny Image Net, 1.0 for Image Net-100, and 0.2 for Image Net-1K. |