https://github.com/chen-hao-chao/dlsm
공식 코드Denoising Likelihood Score Matching for Conditional Score-based Data Generation
Papers with Code 아카이브
Image Generation
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Image Generation (synthesis) is the task of generating new images from an existing dataset.
- Unconditional generation refers to generating samples unconditionally from the dataset, i.e. $p(y)$
- Conditional image generation (subtask) refers to generating samples conditionally from the dataset, based on a label, i.e. $p(y|x)$.
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>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chen-hao-chao/qcsbm
공식 코드On Investigating the Conservative Property of Score-Based Generative Models
https://github.com/chen-xin-94/dart
공식 코드https://github.com/chen742/DCF
공식 코드Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
https://github.com/chen742/PiPa
공식 코드PiPa: Pixel- and Patch-wise Self-supervised Learning for Domain Adaptative Semantic Segmentation
https://github.com/chenboluo/acmf
공식 코드https://github.com/chengaopro/AdversarialNAS
공식 코드AdversarialNAS: Adversarial Neural Architecture Search for GANs
https://github.com/chengenw/backdoorcm
공식 코드https://github.com/chengruogu0915/GeoUni
공식 코드GeoUni: A Unified Model for Generating Geometry Diagrams, Problems and Problem Solutions
https://github.com/chenguolin/WatermarkFaker
공식 코드Watermark Faker: Towards Forgery of Digital Image Watermarking
https://github.com/chenguoz/caig
공식 코드CTR-Driven Advertising Image Generation with Multimodal Large Language Models
https://github.com/chengyiliu-cs/generative-diffusion-models-on-graphs
공식 코드Generative Diffusion Models on Graphs: Methods and Applications
https://github.com/chengzhag/panfusion
공식 코드Taming Stable Diffusion for Text to 360° Panorama Image Generation
https://github.com/chenhaoxing/hdnet
공식 코드https://github.com/chenllliang/dnd-transformer
공식 코드https://github.com/chenllliang/dreamengine
공식 코드https://github.com/chenmingxiang110/VAE-NCA
공식 코드https://github.com/chenqi008/leica
공식 코드Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit Assignment
https://github.com/chenweixin107/trojdiff
공식 코드TrojDiff: Trojan Attacks on Diffusion Models with Diverse Targets
https://github.com/chenwu98/cycle-diffusion
공식 코드Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance
https://github.com/chenwu98/unified-generative-zoo
공식 코드Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance
https://github.com/chenyangzhu1/multibooth
공식 코드MultiBooth: Towards Generating All Your Concepts in an Image from Text
https://github.com/chenyirui/gim
공식 코드GIM: A Million-scale Benchmark for Generative Image Manipulation Detection and Localization
https://github.com/chicychen/loco-edit
공식 코드Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing
https://github.com/chinmay5/vessel_diffuse
공식 코드https://github.com/choi403/diffusionguard
공식 코드DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing
https://github.com/choyingw/Voice2Mesh
공식 코드Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices
https://github.com/chrischen1023/hint
공식 코드HINT: High-quality INPainting Transformer with Mask-Aware Encoding and Enhanced Attention
https://github.com/chrischen1023/mxt
공식 코드https://github.com/chrischen1023/nclg-mt
공식 코드A Feasibility Study on Image Inpainting for Non-cleft Lip Generation from Patients with Cleft Lip
https://github.com/chrischen1023/sem-net
공식 코드SEM-Net: Efficient Pixel Modelling for image inpainting with Spatially Enhanced SSM
https://github.com/chrisdonahue/sdgan
공식 코드Semantically Decomposing the Latent Spaces of Generative Adversarial Networks
https://github.com/chrisdonahue/wavegan
공식 코드https://github.com/chromaica/chromaica.github.io
공식 코드ImagenHub: Standardizing the evaluation of conditional image generation models
https://github.com/chuanli11/CNNMRF
공식 코드Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis
https://github.com/chungimungi/Generation-of-Colors-using-BiLSTMs
공식 코드Generation Of Colors using Bidirectional Long Short Term Memory Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.