https://github.com/faniamokhayeri/DSFS
공식 코드Domain-Specific Face Synthesis for Video Face Recognition from a Single Sample Per Person
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/fast-codi/CoDi
공식 코드CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation
https://github.com/fatemejamalii/e2f-net
공식 코드https://github.com/fau-lms/cade
공식 코드https://github.com/fedenoce/s2l-s2d
공식 코드Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation
https://github.com/fei-hdu/sRender
공식 코드Bridging Unpaired Facial Photos And Sketches By Line-drawings
https://github.com/fei960922/GPointNet
공식 코드https://github.com/feifeiobama/RectifID
공식 코드RectifID: Personalizing Rectified Flow with Anchored Classifier Guidance
https://github.com/feipan664/IntraDA
공식 코드Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
https://github.com/feizc/diffusion-rwkv
공식 코드Diffusion-RWKV: Scaling RWKV-Like Architectures for Diffusion Models
https://github.com/feizc/dis
공식 코드https://github.com/feizc/dit-moe
공식 코드https://github.com/felifri/magbig
공식 코드https://github.com/felixchan9527/versagen_official
공식 코드VersaGen: Unleashing Versatile Visual Control for Text-to-Image Synthesis
https://github.com/fenfenfenfan/VMix
공식 코드VMix: Improving Text-to-Image Diffusion Model with Cross-Attention Mixing Control
https://github.com/fenglinglwb/mat
공식 코드https://github.com/fenglinglwb/psm
공식 코드Image Inpainting via Iteratively Decoupled Probabilistic Modeling
https://github.com/fenglinglwb/sdm
공식 코드Image Inpainting via Iteratively Decoupled Probabilistic Modeling
https://github.com/fengshikun/SLDM
공식 코드Straight-Line Diffusion Model for Efficient 3D Molecular Generation
https://github.com/fergaletto/mssr
공식 코드A deep learning approach for marine snow synthesis and removal
https://github.com/ferry-li/co-erasing
공식 코드https://github.com/ffdd-diffusion/defectdiffu
공식 코드Few-shot Defect Image Generation based on Consistency Modeling
https://github.com/ffhibnese/gifd
공식 코드GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization
https://github.com/fhdnskfbeuv/attackipa
공식 코드Mind the Trojan Horse: Image Prompt Adapter Enabling Scalable and Deceptive Jailbreaking
https://github.com/fibarrola/affect_conditioned_generation
공식 코드https://github.com/fiora6/divtrackee
공식 코드https://github.com/firasgit/medicaldiffusion
공식 코드Medical Diffusion: Denoising Diffusion Probabilistic Models for 3D Medical Image Generation
https://github.com/flagai-open/flagai
공식 코드AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities
https://github.com/flagshippioneering/flow-of-options
공식 코드Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through Options
https://github.com/flame-chasers/syntbpr
공식 코드An Empirical Study of Validating Synthetic Data for Text-Based Person Retrieval
https://github.com/florentinbieder/PatchDDM-3D
공식 코드Memory-Efficient 3D Denoising Diffusion Models for Medical Image Processing
https://github.com/florinshen/vista3d
공식 코드https://github.com/flyingby/awesome-deepfake-generation-and-detection
공식 코드https://github.com/fmp453/rectable
공식 코드https://github.com/fnzhan/AMT
공식 코드Bi-level Feature Alignment for Versatile Image Translation and Manipulation
https://github.com/fnzhan/RABIT
공식 코드Bi-level Feature Alignment for Versatile Image Translation and Manipulation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.