https://github.com/pluslabnlp/vp-csv
공식 코드Character-Centric Story Visualization via Visual Planning and Token Alignment
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/pmh9960/GCDP
공식 코드https://github.com/pmh9960/regft-for-gen
공식 코드Regularized Training with Generated Datasets for Name-Only Transfer of Vision-Language Models
https://github.com/podgorskiy/ALAE
공식 코드https://github.com/point0bar1/ebm-life-cycle
공식 코드EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling
https://github.com/polimi-ispl/jpeg-ai-antifor
공식 코드https://github.com/polimi-ispl/western-blot-synthetic-manipulation-localization
공식 코드Localization of Synthetic Manipulations in Western Blot Images
https://github.com/poloclub/clickdiffusion
공식 코드ClickDiffusion: Harnessing LLMs for Interactive Precise Image Editing
https://github.com/poloclub/diffusion-explainer
공식 코드Diffusion Explainer: Visual Explanation for Text-to-image Stable Diffusion
https://github.com/poopit/dcd-official
공식 코드Discriminator-Cooperated Feature Map Distillation for GAN Compression
https://github.com/pooria90/diffecho
공식 코드Vision-Language Synthetic Data Enhances Echocardiography Downstream Tasks
https://github.com/poppuppy/sar
공식 코드Customize Your Visual Autoregressive Recipe with Set Autoregressive Modeling
https://github.com/potato-kitty/objectadd
공식 코드ObjectAdd: Adding Objects into Image via a Training-Free Diffusion Modification Fashion
https://github.com/ppope/dimensions
공식 코드The Intrinsic Dimension of Images and Its Impact on Learning
https://github.com/prajwalsingh/eegstylegan-ada
공식 코드Learning Robust Deep Visual Representations from EEG Brain Recordings
https://github.com/pranavphoenix/FLD
공식 코드https://github.com/pranavphoenix/WavePaint
공식 코드WavePaint: Resource-efficient Token-mixer for Self-supervised Inpainting
https://github.com/prash29/lager
공식 코드https://github.com/prasunroy/pose-transfer
공식 코드https://github.com/prasunroy/tips
공식 코드https://github.com/prclibo/ice
공식 코드https://github.com/preethiseshadri518/bias-amplification-paradox
공식 코드https://github.com/priv-creation/concept-centric-personalization
공식 코드Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
https://github.com/priv-creation/in-domain-generation-diffusion
공식 코드Image is All You Need to Empower Large-scale Diffusion Models for In-Domain Generation
https://github.com/profjsb/deepCR
공식 코드https://github.com/project-monai/generativemodels
공식 코드Generative AI for Medical Imaging: extending the MONAI Framework
https://github.com/pronobis/libspn-keras
공식 코드https://github.com/proteus1991/pscc-net
공식 코드https://github.com/prs-eth/u-tilise
공식 코드U-TILISE: A Sequence-to-sequence Model for Cloud Removal in Optical Satellite Time Series
https://github.com/psc0628/dm-osvp
공식 코드Exploiting Priors from 3D Diffusion Models for RGB-Based One-Shot View Planning
https://github.com/pschaldenbrand/Frida
공식 코드https://github.com/pshams55/ASPN
공식 코드Road Segmentation for Remote Sensing Images using Adversarial Spatial Pyramid Networks
https://github.com/pshams55/GAN-Case-Study
공식 코드Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies
https://github.com/psyker-team/mist-v2
공식 코드Targeted Attack Improves Protection against Unauthorized Diffusion Customization
https://github.com/puar-playground/TextLap
공식 코드TextLap: Customizing Language Models for Text-to-Layout Planning
https://github.com/puckikk1202/difffacesketch2023
공식 코드DiffFaceSketch: High-Fidelity Face Image Synthesis with Sketch-Guided Latent Diffusion Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.