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..
Learning to See by Looking at Noise
Authors: Manel Baradad Jurjo, Jonas Wulff, Tongzhou Wang, Phillip Isola, Antonio Torralba
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate performance using Imagenet-100 [60] and the Visual Task Adaptation Benchmark [61].Figures 3 and 4 show the performance for the proposed fully generative methods from noise on Imagenet100 and VTAB (Tables can be found in the Sup.Mat.). |
| Researcher Affiliation | Academia | Manel Baradad MIT CSAIL EMAIL Wulff MIT CSAIL EMAIL Wang MIT CSAIL EMAIL Isola MIT CSAIL EMAIL Torralba MIT CSAIL EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide a statement about releasing open-source code or a link to a code repository for their methodology. |
| Open Datasets | Yes | We evaluate performance using Imagenet-100 [60] and the Visual Task Adaptation Benchmark [61].As an upper-bound for the maximum expected performance with synthetic images, we consider the same training procedure but using the following real datasets: 1) Places365 [62] ... 2) STL-10 [63] ... 3) Imagenet1k [1] |
| Dataset Splits | Yes | For each of the datasets in VTAB, we fix the number of training and validation samples to 20k at random for the datasets where there are more samples available. |
| Hardware Specification | No | The paper mentions 'computation resources from the Satori cluster donated by IBM to MIT' but does not provide specific hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions models like 'AlexNet-based encoder', 'MoCo v2', and 'StyleGANv2', but does not list specific software dependencies with version numbers (e.g., 'PyTorch 1.9'). |
| Experiment Setup | Yes | We generate 105k samples using the proposed image models at 128x128 resolution, which are then downsampled to 96x96 and cropped at random to 64x64 before being fed to the encoder.We fix a common set of hyperparameters for all the methods under test to the values found to perform well by the authors of [58]. |