https://github.com/aliprf/ganalyzer
공식 코드GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis
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/alisadeghian/PGMGAN
공식 코드https://github.com/allenai/embodied-clip
공식 코드https://github.com/allenai/unified-io-2
공식 코드Unified-IO 2: Scaling Autoregressive Multimodal Models with Vision, Language, Audio, and Action
https://github.com/allenai/x-lxmert
공식 코드X-LXMERT: Paint, Caption and Answer Questions with Multi-Modal Transformers
https://github.com/alonzoleeeooo/sketchrefiner
공식 코드https://github.com/alpc91/NICE-GAN-pytorch
공식 코드Reusing Discriminators for Encoding: Towards Unsupervised Image-to-Image Translation
https://github.com/alpc91/transda
공식 코드Smoothing Matters: Momentum Transformer for Domain Adaptive Semantic Segmentation
https://github.com/alpha-davidson/sun_cgan
공식 코드Two-dimensional total absorption spectroscopy with conditional generative adversarial networks
https://github.com/alpha-innovator/omnicaptioner
공식 코드https://github.com/alpha-vllm/lumina-image-2.0
공식 코드Lumina-Image 2.0: A Unified and Efficient Image Generative Framework
https://github.com/alpha-vllm/lumina-mgpt
공식 코드https://github.com/alpha-vllm/lumina-t2x
공식 코드Lumina-Next: Making Lumina-T2X Stronger and Faster with Next-DiT
https://github.com/alphanumericslab/ecg-image-kit
공식 코드https://github.com/alphawuseu/mlfm
공식 코드https://github.com/alsdudrla10/DG
공식 코드Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
https://github.com/alsdudrla10/DG_imagenet
공식 코드Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
https://github.com/alvinzhuyx/cdrl
공식 코드Learning Energy-Based Models by Cooperative Diffusion Recovery Likelihood
https://github.com/alxzzhou/MACS
공식 코드MACS: Multi-source Audio-to-image Generation with Contextual Significance and Semantic Alignment
https://github.com/amaha7984/kan-cut
공식 코드https://github.com/amazon-research/gan-control
공식 코드https://github.com/amazon-science/fpgan-control
공식 코드FPGAN-Control: A Controllable Fingerprint Generator for Training with Synthetic Data
https://github.com/amazon-science/masked-diffusion-lm
공식 코드A Cheaper and Better Diffusion Language Model with Soft-Masked Noise
https://github.com/amazon-science/pixels
공식 코드PIXELS: Progressive Image Xemplar-based Editing with Latent Surgery
https://github.com/aminK8/KnobGen
공식 코드KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models
https://github.com/aminK8/TaxaDiffusion
공식 코드TaxaDiffusion: Progressively Trained Diffusion Model for Fine-Grained Species Generation
https://github.com/aminkaramlou/qnlg
공식 코드https://github.com/amirbar/visual_prompting
공식 코드https://github.com/amirhertz/geometric-textures
공식 코드https://github.com/amjltc295/Free-Form-Video-Inpainting
공식 코드Learnable Gated Temporal Shift Module for Deep Video Inpainting
https://github.com/amonroym99/iti-gen-reproducibility
공식 코드Reproducibility Study of "ITI-GEN: Inclusive Text-to-Image Generation"
https://github.com/anciukevicius/renderdiffusion
공식 코드RenderDiffusion: Image Diffusion for 3D Reconstruction, Inpainting and Generation
https://github.com/andreas128/RePaint
공식 코드RePaint: Inpainting using Denoising Diffusion Probabilistic Models
https://github.com/andreas128/SRFlow
공식 코드SRFlow: Learning the Super-Resolution Space with Normalizing Flow
https://github.com/andrefagereng/polyp-gan
공식 코드PolypConnect: Image inpainting for generating realistic gastrointestinal tract images with polyps
https://github.com/andreskarjus/genaiexperiment
공식 코드Expertise elevates AI usage: experimental evidence comparing laypeople and professional artists
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.