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..
Identifiable Contrastive Learning with Automatic Feature Importance Discovery
Authors: Qi Zhang, Yifei Wang, Yisen Wang
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we first verify the identifiability of tri CL and further evaluate the performance of tri CL on real-world datasets including CIFAR-10, CIFAR-100, and Image Net-100. |
| Researcher Affiliation | Academia | Qi Zhang1 Yifei Wang2 Yisen Wang1,3 1 National Key Lab of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University 2 School of Mathematical Sciences, Peking University 3 Institute for Artificial Intelligence, Peking University |
| Pseudocode | No | No, the paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/PKU-ML/Tri-factor-Contrastive-Learning. |
| Open Datasets | Yes | We pretrain the Res Net-18 on CIFAR-10, CIFAR-100 and Image Net-100 [8] by tri CL. |
| Dataset Splits | No | No, the paper does not provide specific training/validation/test dataset splits needed for full reproducibility. It mentions "standard split" for k-NN evaluation but lacks explicit percentages or counts for training, validation, or test sets. |
| Hardware Specification | No | No, the paper does not provide specific hardware details such as CPU/GPU models, processor types, or memory used for running the experiments. |
| Software Dependencies | No | No, the paper does not provide specific software dependencies with version numbers (e.g., library or framework versions like PyTorch 1.9) needed for reproducibility. |
| Experiment Setup | Yes | We adopt Res Net-18 as the backbone. For CIFAR-10 and CIFAR-100, the projector is a two-layer MLP with hidden dimension 2048 and output dimension 256. And for Image Net-100, the projector is a two-layer MLP with hidden dimension 4096 and output dimension 512. We pretrain the models with batch size 256 and weight decay 0.0001. For CIFAR-10 and CIFAR-100, we pretrain the models for 200 epochs. While for Image Net-100, we pretrain the models for 400 epochs. We use the cosine anneal learning rate scheduler and set the initial learning rate to 0.4 on CIFAR-10, CIFAR-100, and 0.3 on Image Net-100. ... We train the linear classifier on 20 dimensions of the frozen networks for 30 epochs during the linear evaluation. We set batch size to 256 and weight decay to 0.0001. |