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..
Improving Transformation Invariance in Contrastive Representation Learning
Authors: Adam Foster, Rattana Pukdee, Tom Rainforth
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approaches first on CIFAR-10 and CIFAR-100 (Krizhevsky et al., 2009), using transformations appropriate to natural images and evaluating on a downstream classification task. To validate that our ideas transfer to other settings, and to use our gradient regularizer within a fully differentiable generative process, we further introduce a new synthetic dataset called Spirograph. |
| Researcher Affiliation | Academia | Adam Foster , Rattana Pukdee & Tom Rainforth Department of Statistics University of Oxford EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | For an open source implementation of our methods, see https://github.com/ae-foster/invclr. |
| Open Datasets | Yes | We evaluate our approaches first on CIFAR-10 and CIFAR-100 (Krizhevsky et al., 2009)... To validate that our ideas transfer to other settings, and to use our gradient regularizer within a fully differentiable generative process, we further introduce a new synthetic dataset called Spirograph. A standalone implementation of this dataset can be found at https://github.com/rattaoup/ spirograph. |
| Dataset Splits | No | The paper states: "The final dataset consists of 100k training and 20k test images of size 32 32." and "We train these linear models with L-BFGS... on the training set and evaluate performance on the test set." No specific validation split or set is mentioned for reproduction. |
| Hardware Specification | Yes | Our experiments were implemented in Py Torch (Paszke et al., 2019) and ran on 8 Nvidia Ge Force GTX 1080Ti GPUs. |
| Software Dependencies | Yes | Our experiments were implemented in Py Torch (Paszke et al., 2019)... |
| Experiment Setup | Yes | Table 4: Hyperparameters used for CIFAR-10, CIFAR-100 and Spirograph. Parameter: Training batch size 512, Training epochs 1000, Optimizer LARS, Scheduler Cosine annealing, Learning rate 3e-3, Momentum 0.9, Temperature τ 0.5. Table 5: Hyperparameters for gradient penalty calculation. Parameter: L 100, λ 0.1/0.01, Clip value 1/1000. |