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..
Parametric Instance Classification for Unsupervised Visual Feature learning
Authors: Yue Cao, Zhenda Xie, Bin Liu, Yutong Lin, Zheng Zhang, Han Hu
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform unsupervised feature pre-training on the most widely-used dataset, Image Net-1K [8], which have 1.28 million training images. For Image Net-1K, we vary the training lengths from 200 epochs to 1600 epochs2 to facilitate comparison with previous reported results. In all experiments, a Res Net-50 [16, 17] model is adopted as the backbone network. Eight GPUs of Titan V100 and a total batch size of 512 are adopted. We follow the similar augmentations and training settings as [5, 6], with details shown in Appendix C. For the cosine soft-max loss (1), we find out that τ = 0.2 could generally perform well thus we adopt it for all experiments. |
| Researcher Affiliation | Collaboration | Yue Cao 1, Zhenda Xie 12, Bin Liu 12, Yutong Lin13, Zheng Zhang1, Han Hu1 1Microsoft Research Asia 2Tsinghua University 3Xi an Jiaotong University EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. It describes the methodology in text and with a diagram (Figure 1). |
| Open Source Code | Yes | The code and network configurations are available at https://github.com/bl0/PIC. |
| Open Datasets | Yes | We perform unsupervised feature pre-training on the most widely-used dataset, Image Net-1K [8], which have 1.28 million training images. |
| Dataset Splits | Yes | The linear evaluation protocol [18, 15, 5] on the Image Net-1k dataset is used in ablations. |
| Hardware Specification | Yes | Eight GPUs of Titan V100 and a total batch size of 512 are adopted. |
| Software Dependencies | No | The paper mentions ResNet-50 models and ImageNet-1K, but it does not specify software dependencies like Python, PyTorch, TensorFlow versions, or other libraries with their version numbers. |
| Experiment Setup | Yes | We perform unsupervised feature pre-training on the most widely-used dataset, Image Net-1K [8], which have 1.28 million training images. For Image Net-1K, we vary the training lengths from 200 epochs to 1600 epochs2 to facilitate comparison with previous reported results. In all experiments, a Res Net-50 [16, 17] model is adopted as the backbone network. Eight GPUs of Titan V100 and a total batch size of 512 are adopted. We follow the similar augmentations and training settings as [5, 6], with details shown in Appendix C. For the cosine soft-max loss (1), we find out that τ = 0.2 could generally perform well thus we adopt it for all experiments. |