https://github.com/betterze/StyleSpace
공식 코드StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation
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/bgu-cs-vil/ditac
공식 코드https://github.com/bhartendu-kumar/pnp-conv
공식 코드https://github.com/bhartidivya/aad-dce
공식 코드https://github.com/bigbench2024/bigbench2024
공식 코드BIGbench: A Unified Benchmark for Evaluating Multi-dimensional Social Biases in Text-to-Image Models
https://github.com/bilican/sparse_peft
공식 코드Exploring Sparsity for Parameter Efficient Fine Tuning Using Wavelets
https://github.com/bing-li-ai/AniGAN
공식 코드AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
https://github.com/binhuixie/spcl
공식 코드https://github.com/binuxliu/nocplace
공식 코드NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer
https://github.com/bioinf-jku/TTUR
공식 코드GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
https://github.com/bioinfomachinelearning/bio-diffusion
공식 코드Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
https://github.com/bioinfomachinelearning/gcdm-sbdd
공식 코드Geometry-Complete Diffusion for 3D Molecule Generation and Optimization
https://github.com/biomedia-mbzuai/tibix
공식 코드TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation
https://github.com/biomedia-mira/counterfactual-contrastive
공식 코드Counterfactual contrastive learning: robust representations via causal image synthesis
https://github.com/biomedia-mira/sos-vae
공식 코드Structured Uncertainty in the Observation Space of Variational Autoencoders
https://github.com/birdortyedi/fashion-image-inpainting
공식 코드A Benchmark for Inpainting of Clothing Images with Irregular Holes
https://github.com/bismex/HiCMD
공식 코드Hi-CMD: Hierarchical Cross-Modality Disentanglement for Visible-Infrared Person Re-Identification
https://github.com/biswassanket/synth_doc_generation
공식 코드DocSynth: A Layout Guided Approach for Controllable Document Image Synthesis
https://github.com/bit-da/sepico
공식 코드SePiCo: Semantic-Guided Pixel Contrast for Domain Adaptive Semantic Segmentation
https://github.com/biweidai/SIG
공식 코드https://github.com/bjing2016/subspace-diffusion
공식 코드https://github.com/blackprotoss/gsdm
공식 코드Text Image Inpainting via Global Structure-Guided Diffusion Models
https://github.com/blinghe/pixelfolder
공식 코드PixelFolder: An Efficient Progressive Pixel Synthesis Network for Image Generation
https://github.com/blt2114/twisted_diffusion_sampler
공식 코드Practical and Asymptotically Exact Conditional Sampling in Diffusion Models
https://github.com/bluedyee/tf-gph
공식 코드https://github.com/bluejangbaljang/shunit
공식 코드SHUNIT: Style Harmonization for Unpaired Image-to-Image Translation
https://github.com/blutjens/eie-earth-public
공식 코드Generating Physically-Consistent Satellite Imagery for Climate Visualizations
https://github.com/bo-wang-up/prime
공식 코드Topology Optimization of Random Memristors for Input-Aware Dynamic SNN
https://github.com/bobo0303/LIGHTWEIGHT-IMAGE-INPAINTING-BY-STRIPE-WINDOW-TRANSFORMER-WITH-JOINT-ATTENTION-TO-CNN
공식 코드Lightweight Image Inpainting by Stripe Window Transformer with Joint Attention to CNN
https://github.com/bonifazstuhr/feamgan
공식 코드Masked Discriminators for Content-Consistent Unpaired Image-to-Image Translation
https://github.com/boo-18/gr-gan
공식 코드https://github.com/boqian-li/GarmentDreamer
공식 코드GarmentDreamer: 3DGS Guided Garment Synthesis with Diverse Geometry and Texture Details
https://github.com/boranhan/geospatial_foundation_models
공식 코드Bridging Remote Sensors with Multisensor Geospatial Foundation Models
https://github.com/borisfrb/MINT
공식 코드https://github.com/boschresearch/Divide-and-Bind
공식 코드Divide & Bind Your Attention for Improved Generative Semantic Nursing
https://github.com/boschresearch/MCCGAN
공식 코드Multi-Class Multi-Instance Count Conditioned Adversarial Image Generation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.