https://github.com/sunzhihao18/ForgerySleuth
공식 코드ForgerySleuth: Empowering Multimodal Large Language Models for Image Manipulation Detection
Papers with Code 아카이브
Image Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/super-jw/LFADEA
공식 코드https://github.com/superboom/dialprompt
공식 코드What Do You Want? User-centric Prompt Generation for Text-to-image Synthesis via Multi-turn Guidance
https://github.com/superhero-7/altdiffuson
공식 코드https://github.com/superrrpotato/Defending-Neural-Backdoors-via-Generative-Distribution-Modeling
공식 코드Defending Neural Backdoors via Generative Distribution Modeling
https://github.com/supersupercong/selfpromer
공식 코드SelfPromer: Self-Prompt Dehazing Transformers with Depth-Consistency
https://github.com/susanqq/Talking_Face_Generation
공식 코드Talking Face Generation by Conditional Recurrent Adversarial Network
https://github.com/sustech-isus/sustechgan
공식 코드SUSTechGAN: Image Generation for Object Detection in Adverse Conditions of Autonomous Driving
https://github.com/susunghong/seg-sdxl
공식 코드Smoothed Energy Guidance: Guiding Diffusion Models with Reduced Energy Curvature of Attention
https://github.com/sutd-visual-computing-group/transferable-forensic-features
공식 코드Discovering Transferable Forensic Features for CNN-generated Images Detection
https://github.com/suxinqi/daad
공식 코드DAAD: Dynamic Analysis and Adaptive Discriminator for Fake News Detection
https://github.com/suxuann/ddib
공식 코드Dual Diffusion Implicit Bridges for Image-to-Image Translation
https://github.com/swhan0329/panorama_image_inpainting
공식 코드PIINET: A 360-degree Panoramic Image Inpainting Network Using a Cube Map
https://github.com/swing-research/funknn
공식 코드https://github.com/switchablenorms/CelebAMask-HQ
공식 코드MaskGAN: Towards Diverse and Interactive Facial Image Manipulation
https://github.com/swizad/split-diffusion
공식 코드Accelerating Guided Diffusion Sampling with Splitting Numerical Methods
https://github.com/swordcheng/FCSR-GAN
공식 코드FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
https://github.com/swsiwu/composition_and_deformance
공식 코드Composition and Deformance: Measuring Imageability with a Text-to-Image Model
https://github.com/sxjdwang/talklip
공식 코드Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert
https://github.com/sxjyjay/lumen
공식 코드Lumen: Unleashing Versatile Vision-Centric Capabilities of Large Multimodal Models
https://github.com/sxjyjay/unitoken
공식 코드UniToken: Harmonizing Multimodal Understanding and Generation through Unified Visual Encoding
https://github.com/sxl142/tex-face
공식 코드Controllable 3D Face Generation with Conditional Style Code Diffusion
https://github.com/syang-genai/mftf
공식 코드MFTF: Mask-free Training-free Object Level Layout Control Diffusion Model
https://github.com/synthrad2023/preprocessing
공식 코드SynthRAD2023 Grand Challenge dataset: generating synthetic CT for radiotherapy
https://github.com/szlbiubiubiu/CemiFace
공식 코드CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition
https://github.com/taehong-moon/ee-diffusion
공식 코드A Simple Early Exiting Framework for Accelerated Sampling in Diffusion Models
https://github.com/taesungp/contrastive-unpaired-translation
공식 코드Contrastive Learning for Unpaired Image-to-Image Translation
https://github.com/taited/sgdiff
공식 코드SGDiff: A Style Guided Diffusion Model for Fashion Synthesis
https://github.com/takagi97/pmt2i
공식 코드Boosting Text-To-Image Generation via Multilingual Prompting in Large Multimodal Models
https://github.com/taki0112/UGATIT
공식 코드https://github.com/takuhirok/CP-GAN
공식 코드https://github.com/takuhirok/NR-GAN
공식 코드https://github.com/taldatech/deep-latent-particles-pytorch
공식 코드Unsupervised Image Representation Learning with Deep Latent Particles
https://github.com/taldatech/soft-intro-vae-pytorch
공식 코드Soft-IntroVAE: Analyzing and Improving the Introspective Variational Autoencoder
https://github.com/talented-q/ifer-master
공식 코드More comprehensive facial inversion for more effective expression recognition
https://github.com/tamarott/ASAPNet
공식 코드Spatially-Adaptive Pixelwise Networks for Fast Image Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.