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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/harlanhong/actalker
공식 코드https://github.com/harlanhong/cvpr2022-dagan
공식 코드Depth-Aware Generative Adversarial Network for Talking Head Video Generation
https://github.com/harlanhong/iccv2023-mcnet
공식 코드https://github.com/harskish/ganspace
공식 코드https://github.com/harvard-ophthalmology-ai-lab/fairdiffusion
공식 코드FairDiffusion: Enhancing Equity in Latent Diffusion Models via Fair Bayesian Perturbation
https://github.com/hasibzunair/adversarial-lesions
공식 코드Melanoma Detection using Adversarial Training and Deep Transfer Learning
https://github.com/hasibzunair/synthetic-covid-cxr-dataset
공식 코드Synthesis of COVID-19 Chest X-rays using Unpaired Image-to-Image Translation
https://github.com/hatchetProject/QuEST
공식 코드QuEST: Low-bit Diffusion Model Quantization via Efficient Selective Finetuning
https://github.com/hazel1994/vipe
공식 코드https://github.com/hazyresearch/scatterbrain
공식 코드Scatterbrain: Unifying Sparse and Low-rank Attention Approximation
https://github.com/hchoi256/i-halla-v1.0
공식 코드Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering
https://github.com/hcmlab/GANterfactual
공식 코드https://github.com/hdspgroup/LD-GAN
공식 코드https://github.com/he-dhamo/simsg
공식 코드https://github.com/hecoding/hyper-modulation
공식 코드Transferring Unconditional to Conditional GANs with Hyper-Modulation
https://github.com/heheyas/v3d
공식 코드https://github.com/hejh8/cyclerdm
공식 코드https://github.com/hejh8/joint-wavelet-and-fourier-priors-guided-diffusion
공식 코드Zero-Shot Low-Light Image Enhancement via Joint Frequency Domain Priors Guided Diffusion
https://github.com/helblazer811/oracle-guided-image-synthesis
공식 코드https://github.com/helblazer811/prefgen
공식 코드PrefGen: Preference Guided Image Generation with Relative Attributes
https://github.com/hellopipu/TC-MGAN
공식 코드https://github.com/hellozicky/ppad
공식 코드Multimodal LLM-Guided Semantic Correction in Text-to-Image Diffusion
https://github.com/hengrui-gu/t2iknowledgeediting
공식 코드https://github.com/hengruilou/deepfacegen
공식 코드A Large-scale Universal Evaluation Benchmark For Face Forgery Detection
https://github.com/hexagonprime/pix2nerf
공식 코드Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation
https://github.com/heyblackC/DFMIR
공식 코드https://github.com/hfmei/idc
공식 코드Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness
https://github.com/hhhhhhao/continuous_tokenizer
공식 코드https://github.com/hhi-aml/histobenchmark
공식 코드https://github.com/hhsinping/svs
공식 코드https://github.com/hhu-mmbs/cedm-official-wavc2025
공식 코드Rethinking cluster-conditioned diffusion models for label-free image synthesis
https://github.com/hiarsal/dae-gan
공식 코드DAE-GAN: Dynamic Aspect-aware GAN for Text-to-Image Synthesis
https://github.com/hichoe95/Artifact-Detection-and-Sequential-Ablation
공식 코드Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?
https://github.com/hidream-ai/hidream-e1
공식 코드HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer
https://github.com/hidream-ai/hidream-i1
공식 코드HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.