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..
Big Self-Supervised Models are Strong Semi-Supervised Learners
Authors: Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey E. Hinton
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We assess the effectiveness of our method on Image Net ILSVRC-2012 [21] with only 1% and 10% of the labeled images available. Our main findings and contributions can be summarized as follows: ... We combine these findings to achieve a new state-of-the-art in semi-supervised learning on Image Net as summarized in Figure 2. |
| Researcher Affiliation | Industry | Ting Chen, Simon Kornblith, Kevin Swersky, Mohammad Norouzi, Geoffrey Hinton Google Research, Brain Team |
| Pseudocode | No | The paper describes the Sim CLRv2 framework and its components verbally and through equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and pretrained checkpoints are available at https://github.com/google-research/simclr. |
| Open Datasets | Yes | evaluate the proposed method on Image Net ILSVRC-2012 [21]. |
| Dataset Splits | Yes | While all 1.28 million images are available, only a randomly sub-sampled 1% (12811) or 10% (128116) of images are associated with labels. (...) For fine-tuning... we fine-tune for 60 epochs with 1% of labels, and 30 epochs with 10% of labels, as well as full Image Net labels. |
| Hardware Specification | Yes | For pretraining, similar to [1], we train our model on 128 Cloud TPUs, with a batch size of 4096 and global batch normalization [33], for total of 800 epochs. |
| Software Dependencies | No | The paper mentions software components like 'LARS optimizer', 'global batch normalization', 'Res Net', and 'Sim CLR' but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | For pretraining... train our model on 128 Cloud TPUs, with a batch size of 4096 and global batch normalization [33], for total of 800 epochs. The learning rate is linearly increased for the first 5% of epochs, reaching maximum of 6.4 (= 0.1 sqrt(Batch Size)), and then decayed with a cosine decay schedule. A weight decay of 1e 4 is used. (...) For fine-tuning... we use a much smaller learning rate, i.e. 0.16 (= 0.005 sqrt(Batch Size)) for standard Res Nets [25], and 0.064 (= 0.002 sqrt(Batch Size)) for larger Res Nets variants (...). A batch size of 1024 is used. Similar to [1], we fine-tune for 60 epochs with 1% of labels, and 30 epochs with 10% of labels, as well as full Image Net labels. For distillation... models are trained for 400 epochs. |