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..
Prototypical Contrastive Predictive Coding
Authors: Kyungmin Lee
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experiments, we validate the effectiveness of our method compared to various supervised and self-supervised knowledge distillation baselines. |
| Researcher Affiliation | Academia | Kyungmin Lee Agency for Defense Development EMAIL |
| Pseudocode | Yes | The Py Torch style pseudo-code for our Proto CPC is demonstrated in Algorithm 1. |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of the described methodology. |
| Open Datasets | Yes | We experiment on CIFAR-100 (Krizhevsky et al., 2009) and Image Net (Deng et al., 2009) |
| Dataset Splits | Yes | Table 3: Top-1 and Top-5 error rates (%) of student network Res Net-18 on Image Net validation set. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU models, CPU models, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Py Torch style pseudocode' but does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | For CIFAR-100, we initialize the learning rate as 0.05, and decay it by 0.1 every 30 epochs after the ο¬rst 150 epochs until the last 240 epoch. [...] Batch size is 64 for CIFAR-100 or 256 for Image Net. [...] For probability of teacher, we use SK operator with 3 steps of iteration and Οt = 0.04. For probability of student, we set Οs = 0.1. The prior momentum for Proto CPC loss is 0.9. We use SGD optimizer with batch size 512 and weight decay is 1e-4. The learning rate is 0.6 and is decayed by cosine learning rate schedule to 1e-6. |