## Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions
Jeremy Ocampo, Matthew Price, Jason McEwen
Keywords: 
ICLR/2023/Proceedings/11399 - Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions.pdf
Project URL: nan

### Implementation
_Given the documentation shared by the authors on a new method, how much effort would it be to re-implement the method from scratch?_

[9]

Not provided, and given link for framework implementation seems to be a scam. Algorithm 1 provides some concrete pseudo code.

### Data
_Given the data description in the documentation, how much effort would it take to either: Find the same data set the authors used, or a similar data set and defend the comparability, or acquire one from scratch?_

[2]

(4/4)

Spherical MNIST, Omni-SYNTHIA, 2D3DS, Pano3D. All cited, described in 5.1/5.2/5.3 and appendix E. No links.

### Configuration 
_Given the (hyper)parameters, including semantic parameters, of the method: How much effort would it take to acquire the algorithm configurations used for obtaining the reported results, and compare them against their computation budget?_

[4]

HP values stated in text per dataset in appendix E. No acquisition.

### Experimental Procedure
_Given the setup of experiments reported in the work, how difficult is it to set up a new experiment with the same procedure, similar to those presented in the original work?_

[2]

Authors measure NR/NR R/R NR/R, mIoU mAcc, L (not all clear). Data plist given in appendix E. Results are single run. Results do not state on which split they report.

### Expertise
_How much effort would it take to acquire the expertise required to reproduce the work independently relying solely on the available documentation?_

[6]

-
