https://github.com/zhaorui-tan/pdf-gan
공식 코드SSD: Towards Better Text-Image Consistency Metric in Text-to-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/zhaorui-tan/sada
공식 코드Semantic-aware Data Augmentation for Text-to-image Synthesis
https://github.com/zhaorw02/FlexiDreamer
공식 코드https://github.com/zhaoyanglyu/early_stopped_ddpm
공식 코드Accelerating Diffusion Models via Early Stop of the Diffusion Process
https://github.com/zhaoyingpan/prompt_log_analysis
공식 코드https://github.com/zhaoyue-zephyrus/bsq-vit
공식 코드Image and Video Tokenization with Binary Spherical Quantization
https://github.com/zhaoyuzhi/childpredictor
공식 코드ChildPredictor: A Child Face Prediction Framework with Disentangled Learning
https://github.com/zhen-dong/magic-me
공식 코드https://github.com/zhendong-wang/prompt-diffusion
공식 코드https://github.com/zhenfeiz/cimd
공식 코드A New Benchmark and Model for Challenging Image Manipulation Detection
https://github.com/zheng-chong/catvton
공식 코드CatVTON: Concatenation Is All You Need for Virtual Try-On with Diffusion Models
https://github.com/zhengchen1999/promptsr
공식 코드https://github.com/zhenglab/ffdbackbone
공식 코드https://github.com/zhengxinyang/sdf-stylegan
공식 코드SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation
https://github.com/zhenrongshen/cellgan
공식 코드CellGAN: Conditional Cervical Cell Synthesis for Augmenting Cytopathological Image Classification
https://github.com/zhenxiong-dl/icassp2025-rcac
공식 코드https://github.com/zhenxuan00/MEM_DGM
공식 코드https://github.com/zhenxuan00/triple-gan
공식 코드https://github.com/zhibaishouheilab/HealthiVert-GAN
공식 코드https://github.com/zhichaowang970201/text-to-video
공식 코드Text-to-Video: a Two-stage Framework for Zero-shot Identity-agnostic Talking-head Generation
https://github.com/zhihefang/SN-DCR/tree/main/SN-DCR-main
공식 코드Spectral Normalization and Dual Contrastive Regularization for Image-to-Image Translation
https://github.com/zhihengli-UR/StyleT2I
공식 코드StyleT2I: Toward Compositional and High-Fidelity Text-to-Image Synthesis
https://github.com/zhijie-group/unicms
공식 코드UniCMs: A Unified Consistency Model For Efficient Multimodal Generation and Understanding
https://github.com/zhipeixu/fakeshield
공식 코드https://github.com/zhiyuan-li-john/mucr
공식 코드https://github.com/zhiyuns/UNITPathSSL
공식 코드https://github.com/zhjohnchan/bert-clip-synesthesia
공식 코드On the Difference of BERT-style and CLIP-style Text Encoders
https://github.com/zhkkke/harmonizer
공식 코드Harmonizer: Learning to Perform White-Box Image and Video Harmonization
https://github.com/zhongniuniu/PAMRFuse-plus
공식 코드https://github.com/zhongping-zhang/sgc_net
공식 코드https://github.com/zhongzero/multi-modal-prompt
공식 코드User-Friendly Customized Generation with Multi-Modal Prompts
https://github.com/zhou-hangyu/allclear
공식 코드AllClear: A Comprehensive Dataset and Benchmark for Cloud Removal in Satellite Imagery
https://github.com/zhouzhouwen/bf-gan
공식 코드https://github.com/zhu-xlab/planet-cr
공식 코드https://github.com/zhuccly/srn
공식 코드Reasoning Structural Relation for Occlusion-Robust Facial Landmark Localization
https://github.com/zhuemann/contextualsegmentation
공식 코드ConTEXTual Net: A Multimodal Vision-Language Model for Segmentation of Pneumothorax
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.