Towards the Generalization of Contrastive Self-Supervised Learning
Authors: Weiran Huang, Mingyang Yi, Xuyang Zhao, Zihao Jiang
ICLR 2023 | Conference PDF | Archive PDF | Plain Text | 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). |