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..
Unsupervised Representation Learning by Predicting Image Rotations
Authors: Spyros Gidaris, Praveer Singh, Nikos Komodakis
ICLR 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We exhaustively evaluate our method in various unsupervised feature learning benchmarks and we exhibit in all of them state-of-the-art performance. |
| Researcher Affiliation | Academia | Spyros Gidaris, Praveer Singh, Nikos Komodakis University Paris-Est, LIGM Ecole des Ponts Paris Tech EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The code and models of our paper will be published on: https://github.com/gidariss/Feature Learning Rot Net. This indicates a future release, not immediate concrete access. |
| Open Datasets | Yes | In this section we conduct an extensive evaluation of our approach on the most commonly used image datasets, such as CIFAR-10 (Krizhevsky & Hinton, 2009), Image Net (Russakovsky et al., 2015), PASCAL (Everingham et al., 2010), and Places205 (Zhou et al., 2014) |
| Dataset Splits | No | The paper mentions 'training' and 'test' sets for datasets like CIFAR-10 and ImageNet, but it does not explicitly describe a 'validation split' or its size/methodology from the overall dataset for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | our Alex Net model trains in around 2 days using a single Titan X GPU |
| Software Dependencies | No | The paper describes the neural network architectures and training optimizers (e.g., SGD, batch-norm, relu units) but does not list specific software libraries or their version numbers (e.g., PyTorch, TensorFlow, etc.). |
| Experiment Setup | Yes | In order to train them on the rotation prediction task, we use SGD with batch size 128, momentum 0.9, weight decay 5e 4 and lr of 0.1. We drop the learning rates by a factor of 5 after epochs 30, 60, and 80. We train in total for 100 epochs. |