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..
Deep Scale-spaces: Equivariance Over Scale
Authors: Daniel Worrall, Max Welling
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate our networks on the Patch Camelyon and Cityscapes datasets, to prove their utility and perform introspective studies to further understand their properties. |
| Researcher Affiliation | Collaboration | Daniel E. Worrall AMLAB, Philips Lab University of Amsterdam EMAIL Max Welling AMLAB, Philips Lab University of Amsterdam EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks were explicitly labeled or formatted. |
| Open Source Code | No | The paper mentions 'deworrall92.github.io' but does not explicitly state that the source code for the described methodology is available there, nor does it provide a direct repository link or specific statement of code release. |
| Open Datasets | Yes | Patch Camelyon [Veeling et al., 2018] and Cityscapes [Cordts et al., 2016] datasets. |
| Dataset Splits | Yes | The Cityscapes dataset [Cordts et al., 2016] contains 2975 training images, 500 validation images, and 1525 test images of resolution 2048 × 1024 px. |
| Hardware Specification | No | The paper mentions training 'split over 4 GPUs' but does not provide specific models or other hardware details (e.g., CPU, memory). |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., libraries, frameworks, or programming language versions) are mentioned. |
| Experiment Setup | Yes | Our training procedure is: 100 epochs SGD, learning rate 0.1 divided by 10 every 40 epochs, momentum 0.9, batch size of 512, split over 4 GPUs. |