Deep Scale-spaces: Equivariance Over Scale
Authors: Daniel Worrall, Max Welling
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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 d.e.worrall@uva.nl Max Welling AMLAB, Philips Lab University of Amsterdam m.welling@uva.nl |
| 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. |