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/nifangbaage/coordfill
공식 코드CoordFill: Efficient High-Resolution Image Inpainting via Parameterized Coordinate Querying
https://github.com/nihukat/concept-conductor
공식 코드Concept Conductor: Orchestrating Multiple Personalized Concepts in Text-to-Image Synthesis
https://github.com/nihukat/freegraftor
공식 코드FreeGraftor: Training-Free Cross-Image Feature Grafting for Subject-Driven Text-to-Image Generation
https://github.com/nimafathi/decodex
공식 코드DeCoDEx: Confounder Detector Guidance for Improved Diffusion-based Counterfactual Explanations
https://github.com/nina-weng/fastdime_med
공식 코드Fast Diffusion-Based Counterfactuals for Shortcut Removal and Generation
https://github.com/ningyu1991/ArtificialGANFingerprints
공식 코드Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
https://github.com/ningyu1991/AttentionDualContrastGAN
공식 코드https://github.com/ningyu1991/GANFingerprints
공식 코드Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints
https://github.com/niopeng/CHIMLE
공식 코드CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
https://github.com/nirvanalan/ln3diff
공식 코드LN3Diff: Scalable Latent Neural Fields Diffusion for Speedy 3D Generation
https://github.com/nithin-gk/at-ddpm
공식 코드https://github.com/nju-pcalab/rag-diffusion
공식 코드Region-Aware Text-to-Image Generation via Hard Binding and Soft Refinement
https://github.com/nju-pcalab/textcrafter
공식 코드TextCrafter: Accurately Rendering Multiple Texts in Complex Visual Scenes
https://github.com/nk-cs-zzl/ian
공식 코드Designing An Illumination-Aware Network for Deep Image Relighting
https://github.com/nkicsl/Fundus_Review
공식 코드https://github.com/nkicsl/resfusion
공식 코드https://github.com/nktoan/causal-inference-via-style-transfer-for-ood-generalisation
공식 코드Causal Inference via Style Transfer for Out-of-distribution Generalisation
https://github.com/nms05/improving-concept-alignment-in-vision-language-concept-bottleneck-models
공식 코드Improving Concept Alignment in Vision-Language Concept Bottleneck Models
https://github.com/nmudur/diffusion-hmc
공식 코드Diffusion-HMC: Parameter Inference with Diffusion-model-driven Hamiltonian Monte Carlo
https://github.com/nnaisense/bayesian-flow-networks
공식 코드https://github.com/noagarcia/phase
공식 코드Uncurated Image-Text Datasets: Shedding Light on Demographic Bias
https://github.com/noamelata/AdaSense
공식 코드https://github.com/noamelata/nesteddiffusion
공식 코드https://github.com/nogu-atsu/small-dataset-image-generation
공식 코드Image Generation From Small Datasets via Batch Statistics Adaptation
https://github.com/noureddinekhiati/semi-supervised-lung-segmentation
공식 코드https://github.com/noxsine/gpt_restoration
공식 코드https://github.com/nubagcilab/diffboost
공식 코드DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model
https://github.com/nupurkmr9/vision-aided-gan
공식 코드https://github.com/nus-hpc-ai-lab/dynamic-diffusion-transformer
공식 코드https://github.com/nust-machine-intelligence-laboratory/mdba
공식 코드Multi-Granularity Denoising and Bidirectional Alignment for Weakly Supervised Semantic Segmentation
https://github.com/nv-tlabs/CLD-SGM
공식 코드Score-Based Generative Modeling with Critically-Damped Langevin Diffusion
https://github.com/nv-tlabs/GENIE
공식 코드https://github.com/nv-tlabs/LION
공식 코드https://github.com/nv-tlabs/padl
공식 코드https://github.com/nvlabs/MUNIT
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.