비지도학습/GAN (27) 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.. SketchGAN: Joint Sketch Completion and Recognition with Generative Adversarial Network SketchGAN: Joint Sketch Completion and Recognition with Generative Adversarial Network SketchGAN : Generative Adversarial Network를 통한 공동 스케치 완성 및 인식 Abstract Hand-drawn sketch recognition is a fundamental problem in computer vision, widely used in sketch-based image and video retrieval, editing, and reorganization. Previous methods often assume that a complete sketch is used as input; however, h.. [GauGAN] Semantic Image Synthesis with Spatially-Adaptive Normalization Semantic Image Synthesis with Spatially-Adaptive Normalization 공간 적응형 정규화를 사용한 의미론적 이미지 합성 Abstract We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, an.. StyleGANv2-ada Training Generative Adversarial Networks with Limited Data Abstract Training generative adversarial networks (GAN) using too little data typically leads to discriminator overfitting, causing training to diverge. We propose an adaptive discriminator augmentation mechanism that significantly stabilizes training in limited data regimes. The approach does not require changes to loss functions or net.. 이전 1 2 3 4 다음
