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..
On the Generalization of Multi-modal Contrastive Learning
Authors: Qi Zhang, Yifei Wang, Yisen Wang
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we propose the first theoretical analysis on the generalization ability of MMCL... Based on this perspective, we compare MMCL and SSCL on real-world data and show that text-induced positive pairs have better semantic consistency and diversity... which validates our understanding of the superiority of multi-modal positive pairs. ...we propose four different techniques and they both bring improvements (as much as 6.2%) on Image Net. |
| Researcher Affiliation | Academia | 1National Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University 2School of Mathematical Sciences, Peking University 3Institute for Artificial Intelligence, Peking University. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github. com/PKU-ML/CLIP-Help-Sim CLR. |
| Open Datasets | Yes | we pretrain the same backbone Vi T-B (Dosovitskiy et al., 2021) on the same dataset, YFCC15M (Thomee et al., 2016; Radford et al., 2021), and evaluate the learned representations on Image Net (Deng et al., 2009a). |
| Dataset Splits | Yes | For efficiency, we randomly draw 1,000 samples from 10 random classes of the Image Net validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like SimCLR, CLIP, ResNet-50, and specific optimizers (LARS, SGD), but it does not provide specific version numbers for these software dependencies or programming languages used. |
| Experiment Setup | Yes | We train the encoder for 100 epochs on Image Net with 512 batch size and use the LARS optimizer with a cosine annealed learning rate schedule. ...we train a linear classifier following the frozen backbones and optimize the Cross Entropy loss with the SGD optimizer. |