Angular Visual Hardness
Authors: Beidi Chen, Weiyang Liu, Zhiding Yu, Jan Kautz, Anshumali Shrivastava, Animesh Garg, Animashree Anandkumar
ICML 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate this score with an indepth and extensive scientific study, and observe that CNN models with the highest accuracy also have the best AVH scores. |
| Researcher Affiliation | Collaboration | 1Rice University 2Georgia Institute of Technology 3NVIDIA 4University of Toronto 5Vector Institute, Toronto 6Caltech. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We conduct expeirments on the Vis DA-17 (Peng et al., 2017) dataset which is a widely used major benchmark for domain adaptation in image classification. The dataset contains a total number of 152, 409 2D synthetic images from 12 categories in the source training set, and 55, 400 real images from MS-COCO (Lin et al., 2014) with the same set of categories as the target domain validation set. |
| Dataset Splits | Yes | We split all the validation images into 5 bins, [0.0, 0.2], [0.2, 0.4], [0.4, 0.6], [0.6, 0.8], [0.8, 1.0], based on their HSF respectively. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers required to reproduce the experiments. |
| Experiment Setup | Yes | For consistency, we train all models for 90 epochs and decay the initial learning rate by a factor of 10 every 30 epochs. The initial learning rate for Alex Net and VGG-19 is 0.01 and for Dense Net-121 and Res Net-50 is 0.1. |