전체 글 (54) 썸네일형 리스트형 MSG-GAN: Multi-Scale Gradients for Generative Adversarial Networks 보호되어 있는 글입니다. [SRGAN]Photo-Realistic Single Image Super-Resolution Using a Generative AdversarialNetwork 보호되어 있는 글입니다. SinGAN: Learning a Generative Model from a Single Natural Image, 2019 SinGAN: Learning a Generative Model from a Single Natural Image,2019 SinGAN : 단일 자연 이미지에서 생성 모델 학습 Abstract We introduce SinGAN, an unconditional generative model that can be learned from a single natural image. Our model is trained to capture the internal distribution of patches within the image, and is then able to generate high quality, diverse samples that carry the same visual content as th.. Improved Consistency Regularization for GANs , 2020 Improved Consistency Regularization for GANs GAN에 대한 향상된 일관성 정규화 Recent work (Zhang 2020) has increased the performance of Generative Adversarial Networks (GANs) by enforcing a consistency cost on the discriminator. We improve on this technique in several ways. We first show that consistency regularization can introduce artifacts into the GAN samples and explain how to fix this issue. We then pr.. StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery Abstract Inspired by the ability of StyleGAN to generate highly realistic images in a variety of domains, much recent work has focused on understanding how to use the latent spaces of StyleGAN to manipulate generated and real images. However, discovering semantically meaningful latent manipulations typically involves painstaking human exami.. Learning Facial Expressions with 3D Mesh Convolutional Neural Network Learning Facial Expressions with 3D Mesh Convolutional Neural Network Making machines understand human expressions enables various useful applications in human-machine interaction. In this article, we present a novel facial expression recognition approach with 3D Mesh Convolutional Neural Networks (3DMCNN) and a visual analytics-guided 3DMCNN design and optimization scheme. From an RGBD camera, .. Point cloud based deep convolutional neural network for 3D face recognition Point cloud based deep convolutional neural network for 3D face recognition 3D 얼굴 인식을위한 포인트 클라우드 기반 심층 컨볼 루션 신경망 Abstract Face recognition is a challenging task as it has to deal with several issues such as illumination, orientation, and variability among the different faces. Previous works have shown that 3D face is a robust biometric trait, and is less sensitive to light and pose variations. A.. Surface Feature Detection and Description with Applications to Mesh Matching, 2009 Surface Feature Detection and Description with Applications to Mesh Matching 메시 매칭에 대한 애플리케이션을 통한 표면 특징 감지 및 설명 Abstract In this paper we revisit local feature detectors/descriptors developed for 2D images and extend them to the more general framework of scalar fields defined on 2D manifolds. We provide methods and tools to detect and describe features on surfaces equiped with scalar functions, .. 이전 1 2 3 4 ··· 7 다음 목록 더보기
