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/danivelikova/lotus
공식 코드https://github.com/danny0628/fedcar
공식 코드FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning
https://github.com/danruta/neat
공식 코드https://github.com/daooshee/hd-vg-130m
공식 코드Swap Attention in Spatiotemporal Diffusions for Text-to-Video Generation
https://github.com/dariant/BiOcularGAN
공식 코드BiOcularGAN: Bimodal Synthesis and Annotation of Ocular Images
https://github.com/dariant/id-booth
공식 코드ID-Booth: Identity-consistent Face Generation with Diffusion Models
https://github.com/dariopavllo/style-semantics
공식 코드Controlling Style and Semantics in Weakly-Supervised Image Generation
https://github.com/darkbblue/generic-diffusion-feature
공식 코드Not All Diffusion Model Activations Have Been Evaluated as Discriminative Features
https://github.com/darkgeekms/retratista
공식 코드StyleT2F: Generating Human Faces from Textual Description Using StyleGAN2
https://github.com/dasayan05/stroke-ae
공식 코드BézierSketch: A generative model for scalable vector sketches
https://github.com/datar001/awesome-ad-on-t2idm
공식 코드Adversarial Attacks and Defenses on Text-to-Image Diffusion Models: A Survey
https://github.com/datar001/revealing-vulnerabilities-in-stable-diffusion-via-targeted-attacks
공식 코드Revealing Vulnerabilities in Stable Diffusion via Targeted Attacks
https://github.com/david-friede/lddr
공식 코드https://github.com/david-stojanovski/echo_from_noise
공식 코드https://github.com/davidbau/dissect
공식 코드Understanding the Role of Individual Units in a Deep Neural Network
https://github.com/davide-belli/context-encoding-chest-xrays
공식 코드https://github.com/davide-coccomini/deepfake-detection-without-deepfakes-generalization-via-synthetic-frequency-patterns-injection
공식 코드Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection
https://github.com/davidelobba/TEMU-VTOFF
공식 코드Inverse Virtual Try-On: Generating Multi-Category Product-Style Images from Clothed Individuals
https://github.com/davidhalladay/frido
공식 코드Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesis
https://github.com/dazinovic/neural-rgbd-surface-reconstruction
공식 코드https://github.com/dbash/lasagna
공식 코드Lasagna: Layered Score Distillation for Disentangled Object Relighting
https://github.com/dbash/pix2pix_cyclegan_guess_noise
공식 코드https://github.com/dbolya/tomesd
공식 코드https://github.com/dbsxodud-11/PAG
공식 코드Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation
https://github.com/dcdcvgroup/layout-diffusion-mindspore
공식 코드LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generation
https://github.com/ddgoodgood/trce
공식 코드TRCE: Towards Reliable Malicious Concept Erasure in Text-to-Image Diffusion Models
https://github.com/ddw2aigroup2cqupt/face-makeup
공식 코드Face-MakeUp: Multimodal Facial Prompts for Text-to-Image Generation
https://github.com/ddz16/UIEDP
공식 코드https://github.com/deanhazineh/multi-image-synthesis
공식 코드Polarization Multi-Image Synthesis with Birefringent Metasurfaces
https://github.com/decurtoydiaz/signatures
공식 코드Signature and Log-signature for the Study of Empirical Distributions Generated with GANs
https://github.com/deep-mi/yoda
공식 코드https://github.com/deep-optimization/morse
공식 코드Morse: Dual-Sampling for Lossless Acceleration of Diffusion Models
https://github.com/deep-unlearn/Big_Data_From_Space_2017
공식 코드https://github.com/deepaksridhar/promptsliders
공식 코드Prompt Sliders for Fine-Grained Control, Editing and Erasing of Concepts in Diffusion Models
https://github.com/deepalianeja/CharacterLipSync2D
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.