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..
Towards the Generalization of Contrastive Self-Supervised Learning
Authors: Weiran Huang, Mingyang Yi, Xuyang Zhao, Zihao Jiang
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct various experiments on the real-world datasets and observe that the downstream performance of contrastive SSL is highly correlated to the concentration of augmented data in Section 5. Our experiments are conducted on CIFAR-10 and CIFAR-100 (Krizhevsky, 2009). |
| Researcher Affiliation | Collaboration | Weiran Huang1 Mingyang Yi2 Xuyang Zhao3 Zihao Jiang1 1 Qing Yuan Research Institute, Shanghai Jiao Tong University 2 Huawei Noah s Ark Lab 3 School of Mathematical Sciences, Peking University |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | No explicit statement about releasing source code or link to a code repository is provided. |
| Open Datasets | Yes | Our experiments are conducted on CIFAR-10 and CIFAR-100 (Krizhevsky, 2009). |
| Dataset Splits | No | Our experiments are conducted on CIFAR-10 and CIFAR-100 (Krizhevsky, 2009). ... Each model is trained with a batch size of 512 and 800 epochs. To evaluate the quality of the encoder, we follow the KNN evaluation protocol (Wu et al., 2018). No explicit mention of train/validation/test dataset splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) are provided for the experimental setup. |
| Software Dependencies | No | The paper mentions algorithms and models like "ResNet-18" and "Sim CLR", but does not specify software dependencies with version numbers (e.g., PyTorch 1.9, CUDA 11.1). |
| Experiment Setup | Yes | We use Res Net-18 (He et al., 2016) as the encoder, and the other settings such as projection head remain the same as the original settings of algorithms. Each model is trained with a batch size of 512 and 800 epochs. We compose all 5 kinds of transformations together, and then successively drop one of the composed transformations from (e) to (b) to conduct 5 experiments for each dataset (Table 1). |