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..
Understanding Deep Contrastive Learning via Coordinate-wise Optimization
Authors: Yuandong Tian
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, this formulation, named Pairweighed Contrastive Learning (α-CL), when coupled with various regularization terms, yields novel contrastive losses that show comparable (or better) performance in CIFAR10 (Krizhevsky et al., 2009) and STL-10 (Coates et al., 2011).Initial experiments (Sec. 6) show that α-CL gives comparable (or even better) downstream performance in CIFAR10 and STL-10, compared to vanilla Info NCE loss.We evaluate our α-CL framework (Def. 1) in CIFAR10 (Krizhevsky et al., 2009) and STL-10 (Coates et al., 2011) with Res Net18 (He et al., 2016), and compare the downstream performance of multiple losses. |
| Researcher Affiliation | Industry | Yuandong Tian Meta AI (FAIR) EMAIL |
| Pseudocode | No | The paper does not contain any explicitly labeled pseudocode or algorithm blocks, nor does it present structured steps formatted like code. |
| Open Source Code | Yes | Codes are available 1. 1https://github.com/facebookresearch/luckmatters/tree/main/ssl/real-dataset |
| Open Datasets | Yes | Empirically, this formulation... yields novel contrastive losses that show comparable (or better) performance in CIFAR10 (Krizhevsky et al., 2009) and STL-10 (Coates et al., 2011).We evaluate our α-CL framework (Def. 1) in CIFAR10 (Krizhevsky et al., 2009) and STL-10 (Coates et al., 2011) with Res Net18 (He et al., 2016), and compare the downstream performance of multiple losses, with regularizers taking the form of R(α) = P j =i r(αij) with a constraint P j =i αij = 1.Tbl. 2 shows more experiments with different backbones (e.g., Res Net50) and more complicated datasets (e.g., CIFAR-100). |
| Dataset Splits | No | The paper mentions using CIFAR10, STL-10, and CIFAR-100 datasets for experiments but does not explicitly provide information on train/validation/test splits (e.g., percentages, sample counts, or specific predefined split references for validation sets). |
| Hardware Specification | No | The paper states: 'Code is written in Py Torch and a single modern GPU suffices for the experiments.' This does not provide specific hardware details such as the GPU model, CPU type, or memory specifications. |
| Software Dependencies | No | The paper mentions 'Code is written in Py Torch' and states 'All training is performed with Adam (Kingma & Ba, 2014) optimizer,' but it does not specify version numbers for PyTorch or any other software libraries or dependencies. |
| Experiment Setup | Yes | Table 1: 'Batchsize 128. Top-1 accuracy with linear evaluation protocol. Temperature τ = 0.5 and learning rate is 0.01.' Table 2: 'For Res Net18, learning rate is 0.01; for Res Net50, learning rate is 0.001.' The paper also mentions '100 epochs 300 epochs 500 epochs'. |