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..
SGDR: Stochastic Gradient Descent with Warm Restarts
Authors: Ilya Loshchilov, Frank Hutter
ICLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically study its performance on the CIFAR-10 and CIFAR-100 datasets, where we demonstrate new state-of-the-art results at 3.14% and 16.21%, respectively. We also demonstrate its advantages on a dataset of EEG recordings and on a downsampled version of the Image Net dataset. |
| Researcher Affiliation | Academia | Ilya Loshchilov & Frank Hutter University of Freiburg Freiburg, Germany, EMAIL |
| Pseudocode | No | The paper provides equation (5) for the learning rate schedule, but it does not present a structured pseudocode or algorithm block. |
| Open Source Code | Yes | Our source code is available at https://github.com/loshchil/SGDR |
| Open Datasets | Yes | The CIFAR-10 and CIFAR-100 datasets (Krizhevsky, 2009) consist of 32 32 color images drawn from 10 and 100 classes, respectively, split into 50,000 train and 10,000 test images. |
| Dataset Splits | No | The paper explicitly mentions "50,000 train and 10,000 test images" for CIFAR-10/100, but does not provide specific details (percentages, sample counts) for a separate validation split. It discusses "validation error" in the discussion, but without defining a split for it. |
| Hardware Specification | No | The paper does not specify any hardware details like CPU, GPU models, or memory used for the experiments. It mentions "high-performance GPUs" generally but no specifics. |
| Software Dependencies | No | The paper mentions using "SGD with Nesterov s momentum" and WRNs, but it does not specify any software libraries (e.g., PyTorch, TensorFlow) or their version numbers. |
| Experiment Setup | Yes | For training, Zagoruyko & Komodakis (2016) used SGD with Nesterov s momentum with initial learning rate set to η0 = 0.1, weight decay to 0.0005, dampening to 0, momentum to 0.9 and minibatch size to 128. The learning rate is dropped by a factor of 0.2 at 60, 120 and 160 epochs, with a total budget of 200 epochs. |