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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Debiased Contrastive Learning
Authors: Ching-Yao Chuang, Joshua Robinson, Yen-Chen Lin, Antonio Torralba, Stefanie Jegelka
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 16,317 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,781 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 | Empirically, the proposed objective consistently outperforms the state-of-the-art for representation learning in vision, language, and reinforcement learning benchmarks. |
| Researcher Affiliation | Academia | Ching-Yao Chuang, Joshua Robinson, Lin Yen-Chen Antonio Torralba, Stefanie Jegelka CSAIL, Massachusetts Institute of Technology Cambridge, MA 02139, USA EMAIL |
| Pseudocode | Yes | Figure 3: Pseudocode for debiased objective with M = 1. The implementation only requires a small modification of the code. We can simply extend the code to debiased objective with M > 1 by changing the pos in line 8 with an average of exponentials for M positive samples. |
| Open Source Code | Yes | The code is available at https://github.com/chingyaoc/DCL. |
| Open Datasets | Yes | First, for CIFAR10 [27] and STL10 [7], we implement Sim CLR [2] with Res Net-50 [17] as the encoder architecture and use the Adam optimizer [23] with learning rate 0.001. ... Following [40], we test our approach on Image Net-100, a randomly chosen subset of 100 classes of Imagenet. ... We use the Book Corpus dataset [25]... |
| Dataset Splits | Yes | All the models are trained for 400 epochs and evaluated by training a linear classifier after fixing the learned embedding. ... 10-fold cross validation is used in testing the performance for binary classification tasks (MR, CR, SUBJ, MPQA). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions software components such as 'Sim CLR', 'Res Net-50', and 'Adam optimizer', but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | Following [2], we set the temperature to t = 0.5 and the dimension of the latent vector to 128. All the models are trained for 400 epochs and evaluated by training a linear classifier after fixing the learned embedding. ... use the Adam optimizer [23] with learning rate 0.001. |