https://github.com/Mind23-2/MindCode-31
Very Deep Convolutional Networks for Large-Scale Image Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Image Generation (synthesis) is the task of generating new images from an existing dataset.
In this section, you can find state-of-the-art leaderboards for unconditional generation. For conditional generation, and other types of image generations, refer to the subtasks.
<span style="color:grey; opacity: 0.6">( Image credit: StyleGAN )</span>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Very Deep Convolutional Networks for Large-Scale Image Recognition
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Image-to-Image Translation with Conditional Adversarial Networks
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
Image-to-Image Translation with Conditional Adversarial Networks
SinGAN: Learning a Generative Model from a Single Natural Image
StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation
SinGAN: Learning a Generative Model from a Single Natural Image
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Your ViT is Secretly a Hybrid Discriminative-Generative Diffusion Model
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Image-to-Image Translation with Conditional Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
Image Inpainting via Conditional Texture and Structure Dual Generation
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Neural Photo Editing with Introspective Adversarial Networks
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.