https://github.com/purdue-m2/robust_dm_generated_image_detection
공식 코드Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 23
- 평가표
- 93
- 보존된 평가 기록
- 870
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/purmavishnuvardhanreddy/genselfdiff-his
공식 코드https://github.com/py85252876/uvd
공식 코드https://github.com/pytorch/fairseq
공식 코드Training with Quantization Noise for Extreme Model Compression
https://github.com/q-future/q-refine
공식 코드G-Refine: A General Quality Refiner for Text-to-Image Generation
https://github.com/qgzhan/esvae
공식 코드ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling
https://github.com/qiangwang57/coreg_flim_histology
공식 코드https://github.com/qianmingduowan/AIM2020-bokeh-BGNet
공식 코드BGGAN: Bokeh-Glass Generative Adversarial Network for Rendering Realistic Bokeh
https://github.com/qianmingduowan/Sat2Density
공식 코드Sat2Density: Faithful Density Learning from Satellite-Ground Image Pairs
https://github.com/qianwangx/instructedit
공식 코드InstructEdit: Improving Automatic Masks for Diffusion-based Image Editing With User Instructions
https://github.com/qianwangx/mdp-diffusion
공식 코드MDP: A Generalized Framework for Text-Guided Image Editing by Manipulating the Diffusion Path
https://github.com/qianyuzqy/CAMix
공식 코드Context-Aware Mixup for Domain Adaptive Semantic Segmentation
https://github.com/qianyuzqy/RCCR
공식 코드Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
https://github.com/qihao067/DiMR
공식 코드https://github.com/qihao067/direct3d
공식 코드DIRECT-3D: Learning Direct Text-to-3D Generation on Massive Noisy 3D Data
https://github.com/qinenergy/corda
공식 코드Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
https://github.com/qinghew/CharacterFactory
공식 코드CharacterFactory: Sampling Consistent Characters with GANs for Diffusion Models
https://github.com/qinghew/StableIdentity
공식 코드StableIdentity: Inserting Anybody into Anywhere at First Sight
https://github.com/qinnzou/mural-image-inpainting
공식 코드https://github.com/qinpengcui/er-sde-solver
공식 코드Elucidating the solution space of extended reverse-time SDE for diffusion models
https://github.com/qinyu-allen-zhao/arinar
공식 코드ARINAR: Bi-Level Autoregressive Feature-by-Feature Generative Models
https://github.com/qinyu-allen-zhao/disa
공식 코드DiSA: Diffusion Step Annealing in Autoregressive Image Generation
https://github.com/qiukunpeng/siamese-diffusion
공식 코드Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
https://github.com/qiuqiangkong/gan_separation_deconvolution
공식 코드Single-Channel Signal Separation and Deconvolution with Generative Adversarial Networks
https://github.com/qiuyu96/carver
공식 코드Benchmarking and Analyzing 3D-aware Image Synthesis with a Modularized Codebase
https://github.com/qiuyu96/codef
공식 코드CoDeF: Content Deformation Fields for Temporally Consistent Video Processing
https://github.com/qiyu3816/DiffSG
공식 코드DiffSG: A Generative Solver for Network Optimization with Diffusion Model
https://github.com/qizekun/vpp
공식 코드VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation
https://github.com/qqlu/entity
공식 코드UniGS: Unified Representation for Image Generation and Segmentation
https://github.com/qrzou/ILSGAN
공식 코드ILSGAN: Independent Layer Synthesis for Unsupervised Foreground-Background Segmentation
https://github.com/qsh-zh/DiffFlow
공식 코드https://github.com/quaife/FDV_Data
공식 코드Fire Dynamic Vision: Image Segmentation and Tracking for Multi-Scale Fire and Plume Behavior
https://github.com/qualcomm-ai-research/clockwork-diffusion
공식 코드Clockwork Diffusion: Efficient Generation With Model-Step Distillation
https://github.com/quantumiracle/consistency_model_for_reinforcement_learning
공식 코드Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning
https://github.com/quickjkee/eff-diff-edit
공식 코드Towards Real-time Text-driven Image Manipulation with Unconditional Diffusion Models
https://github.com/quqixun/bcistainer
공식 코드Breast Cancer Immunohistochemical Image Generation: a Benchmark Dataset and Challenge Review
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.