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..
You Only Train Once: Loss-Conditional Training of Deep Networks
Authors: Alexey Dosovitskiy, Josip Djolonga
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the proposed method both quantitatively and qualitatively on three problems with multiterm loss functions: β-VAE, learned image compression, and fast style transfer.4 EXPERIMENTS |
| Researcher Affiliation | Industry | Alexey Dosovitskiy & Josip Djolonga Google Research, Brain Team EMAIL |
| Pseudocode | No | The paper describes the method in prose and equations but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code will be released at www.github.com/google-research/google-research/yoto. |
| Open Datasets | Yes | We consider two settings: the CIFAR-10 dataset (Krizhevsky, 2009) with Gaussian outputs, and the Shapes3D dataset (Burgess & Kim, 2018) with Bernoulli outputs. We evaluate the compression models on two datasets: Kodak (Kodak, 1993) and Tecnick (Asuni & Giachetti, 2014). We sample the content images form Image Net (Deng et al., 2009) and use 14 pointillism paintings as the style images. |
| Dataset Splits | Yes | We select the fixed β so that it minimizes the average validation loss over all β values. Figure 7: Qualitative comparison of image stylization models on an image from the validation set of Image Net. |
| Hardware Specification | No | The paper mentions "on a single CPU core" in the context of timing a specific comparison, but it does not provide specific hardware details (like GPU models or CPU models with speeds) for the main experiments. |
| Software Dependencies | No | The paper mentions non-linearities and optimization techniques but does not provide specific version numbers for software dependencies or libraries used (e.g., PyTorch, TensorFlow, Python versions). |
| Experiment Setup | Yes | On Shapes3D we train all models for a total of 600,000 mini-batch iterations, and we multiply the learning rate by 0.5 after 300,000, 390,000, 480,000, and 570,000 iterations. We tuned the learning rates by sweeping over the values {5·10-5, 1·10-4, 2·10-4, 4·10-4, 8·10-4} and ended up using the learning rates 1·10-4 on CIFAR-10 and 2·10-4 on Shapes3D. We use mini-batches of 128 samples on CIFAR-10 and 64 samples on Shapes3D. We use weight decay of 10-5 in all models. |