GAN3 GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes, CVPR’19 논문 링크 : https://openaccess.thecvf.com/content_CVPR_2020/papers/Corona_GanHand_Predicting_Human_Grasp_Affordances_in_Multi-Object_Scenes_CVPR_2020_paper.pdf1. IntroductionIn order to predict feasible human grasps, we introduce GanHand, a multi-task GAN architecture that given solely one input image: 1) estimates the 3D shape/pose of the objects; 2) predicts the best grasp type according to a taxo.. 2023. 4. 6. Generative Adversarial Networks, NIPS’14 논문 링크 : https://papers.nips.cc/paper/5423-generative-adversarial-netsAbstractWe simultaneously train two models: a generative model G that captures the data distribution, and a discriminative model D that estimates the probability that a sample came from the training data rather than G. The training procedure for G is to maximize the probability of D making a mistake. IntroductionIn the proposed.. 2023. 3. 26. DCGAN, ICLR’16 논문 링크 : https://arxiv.org/abs/1511.064341. IntroductionIn this paper, we make the following contributionsWe propose and evaluate a set of constraints on the architectural topology of Convolutional GANs that make them stable to train in most settings. We name this class of architectures Deep Convolutional GANs (DCGAN)We use the trained discriminators for image classification tasks, showing compet.. 2023. 3. 26. 이전 1 다음