Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Do Generated Data Always Help Contrastive Learning?
Authors: Yifei Wang, Jizhe Zhang, Yisen Wang
ICLR 2024 | Venue PDF | LLM Run Details | Input Tokens: 21,836 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,836 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments show that the proposed approach improves downstream accuracy significantly at no extra cost, and it is particularly beneficial for data-scarce scenarios. |
| Researcher Affiliation | Academia | 1 School of Mathematical Sciences, Peking University 2 Institute of Artificial Intelligence and Robotics, Xi an Jiaotong University 3 National Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University 4 Institute for Artificial Intelligence, Peking University |
| Pseudocode | No | No pseudocode or algorithm blocks are present in the paper. |
| Open Source Code | Yes | Code is available at https://github.com/PKU-ML/adainf. |
| Open Datasets | Yes | We conduct experiments on three benchmark datasets: CIFAR-10, CIFAR-100, and Tiny Image Net. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test splits or mention the use of a validation set for hyperparameter tuning. It focuses on training and testing data. |
| Hardware Specification | Yes | All of the models are pretrained with 4 NVIDIA GTX 3090 GPUs. |
| Software Dependencies | No | The paper mentions using the "solo-learn library (da Costa et al., 2022)" but does not specify a version number for this or any other software dependency. |
| Experiment Setup | Yes | For a fair comparison of inflated and non-inflated training, we train the model for 100k steps in all cases... Specifically, we weaken two most important augmentations: the min scale of random resized cropping improves from 0.08 to 0.2; the Color Jitter strength decreases from 1 to 0.5; and the probability of applying Color Jitter decreases from 0.8 to 0.4. |