CR-GAN: Learning Complete Representations for Multi-view Generation
Authors: Yu Tian, Xi Peng, Long Zhao, Shaoting Zhang, Dimitris N. Metaxas
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results prove that CR-GAN significantly outperforms stateof-the-art methods, especially when generating from unseen inputs in wild conditions. 4 Experiments CR-GAN aims to learn complete representations in the embedding space. We achieve this goal by combining the two-pathway architecture with self-supervised learning. We conduct experiments to evaluate these two contributions respectively. Then we compare our CR-GAN with DR-GAN [Tran et al., 2017], both the visual results and t-SNE visualization in the embedding space are shown. We also compare CR-GAN and Bi GAN with an image reconstruction task. |
| Researcher Affiliation | Academia | Yu Tian1, Xi Peng1, Long Zhao1, Shaoting Zhang2 and Dimitris N. Metaxas1 1 Rutgers University 2 University of North Carolina at Charlotte {yt219, px13, lz311, dnm}@cs.rutgers.edu, szhang16@uncc.edu |
| Pseudocode | Yes | Algorithm 1: Supervised training with two paths |
| Open Source Code | Yes | 1 The code and pre-trained models are publicly available: https://github.com/bluer555/CR-GAN |
| Open Datasets | Yes | Multi-PIE [Gross et al., 2010] is a labeled dataset collected under constrained environment. We use 250 subjects from the first session with 9 poses within 60 , 20 illuminations, and two expressions. The first 200 subjects are for training and the rest 50 for testing. 300w LP [Zhu et al., 2016] is augmented from 300W [Sagonas et al., 2013] by the face profiling approach [Zhu et al., 2016], which contains view labels as well. We employ images with yaw angles ranging from 60 to +60 , and discretize them into 9 intervals. For evaluation on unlabeled datasets, we use Celeb A [Liu et al., 2015] and IJB-A [Klare et al., 2015]. |
| Dataset Splits | No | The paper states, 'The first 200 subjects are for training and the rest 50 for testing' for Multi-PIE, but does not explicitly provide details about a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific details on the hardware used, such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using WGAN-GP and Adam optimizer but does not specify version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | During training, we set v to be a one-hot vector with 9 dimensions and z [ 1, 1]119 in the latent space. The batch size is 64. Adam optimizer [Kingma and Ba, 2015] is used with the learning rate of 0.0005 and momentum of [0, 0.9]. According to the setting of WGAN-GP, we let λ1 = 10, λ2 λ4 = 1, λ5 = 0.01. Moreover, all the networks are trained after 25 epochs in supervised learning; we train 10 more epochs in self-supervised learning. |