
## Outlier-aware Slicing for Post-Training Quantization in Vision Transformer
Yuexiao Ma, Huixia Li, Xiawu Zheng, Feng Ling, Xuefeng Xiao, Rui Wang, Shilei Wen, Fei Chao, Rongrong Ji
Keywords: 
ICML/2024/Proceedings/33935 - Outlier-aware Slicing for Post-Training Quantization in Vision Transformer.pdf
Project URL: nan

### Implementation
_Given the documentation given by the authors on the method, how much time investment would it be to re-implement the method from scratch?_

[10]

The authors do not share their implementation. The authors provide overviews on the method in figure 1.

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

[6]

(1/1)

The authors state the data set used but provide nearly no details on it.

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

[2]

The authors discuss hyperparameters in 4.1. and refer to appendix F for mroe details. Here they state the values, but a full summary is missing on the HP space. The authors state they have been taken from their original works.

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

[3]

The authors do not state how the dataset is used for training/testing. The metrics are top-1 accuracy and the loss. The results are single models.

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

[5]

Requires experience with (vision) transformers and quantisation.
