Papers with Code 아카이브
Image Inpainting
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tsingqguo/jpgnet
공식 코드JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
https://github.com/tsingqguo/misf
공식 코드MISF: Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting
https://github.com/tsunghan-wu/depth-completion
공식 코드Indoor Depth Completion with Boundary Consistency and Self-Attention
https://github.com/tzm-tora/Stroke-Based-Scene-Text-Erasing
공식 코드Stroke-Based Scene Text Erasing Using Synthetic Data for Training
https://github.com/ucla-starai/tiramisu
공식 코드https://github.com/ucsb-nlp-chang/copaint
공식 코드Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models
https://github.com/uhlerlab/ntk_matrix_completion
공식 코드Simple, Fast, and Flexible Framework for Matrix Completion with Infinite Width Neural Networks
https://github.com/usstdqq/deep-adaptive-sampling-mask
공식 코드https://github.com/utcsilab/ambient-diffusion-mri
공식 코드https://github.com/val-iisc/Reflecting-Reality
공식 코드Reflecting Reality: Enabling Diffusion Models to Produce Faithful Mirror Reflections
https://github.com/vasatdan/wgain-inpaint
공식 코드Image Inpainting Using Wasserstein Generative Adversarial Imputation Network
https://github.com/vdeborto/cdsb
공식 코드https://github.com/vglsd/AIM2020-Image-Inpainting-Challenge
공식 코드https://github.com/vickyFox/Region-wise-Inpainting
공식 코드Region-wise Generative Adversarial ImageInpainting for Large Missing Areas
https://github.com/vinairesearch/lfm
공식 코드https://github.com/vita-group/neural-implicit-dict
공식 코드https://github.com/vivitsai/PiiGAN
공식 코드PiiGAN: Generative Adversarial Networks for Pluralistic Image Inpainting
https://github.com/vl2g/sketch-inpainting
공식 코드Sketch-guided Image Inpainting with Partial Discrete Diffusion Process
https://github.com/vsitzmann/siren
공식 코드Implicit Neural Representations with Periodic Activation Functions
https://github.com/wanghailin97/Guaranteed-Tensor-Recovery-Fused-Low-rankness-and-Smoothness
공식 코드Guaranteed Tensor Recovery Fused Low-rankness and Smoothness
https://github.com/wds1998/Edge-LBAM
공식 코드Image Inpainting with Edge-guided Learnable Bidirectional Attention Maps
https://github.com/wendongzh/spn
공식 코드Fully Context-Aware Image Inpainting with a Learned Semantic Pyramid
https://github.com/wenjuan7275/matir
공식 코드https://github.com/wuyangluo/reffaceinpainting
공식 코드Reference-Guided Large-Scale Face Inpainting with Identity and Texture Control
https://github.com/wyhuai/ddnm
공식 코드Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model
https://github.com/xavierjiezou/diffcr
공식 코드DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images
https://github.com/xavierjiezou/pmaa
공식 코드https://github.com/xiefan-guo/ctsdg
공식 코드Image Inpainting via Conditional Texture and Structure Dual Generation
https://github.com/xinychen/transdim
공식 코드Laplacian Convolutional Representation for Traffic Time Series Imputation
https://github.com/xmindflow/INCODE
공식 코드INCODE: Implicit Neural Conditioning with Prior Knowledge Embeddings
https://github.com/xuehy/depthInpainting
공식 코드Depth Image Inpainting: Improving Low Rank Matrix Completion with Low Gradient Regularization
https://github.com/xufangchn/glf-cr
공식 코드https://github.com/xyfjason/hcl
공식 코드Hierarchical Contrastive Learning for Pattern-Generalizable Image Corruption Detection
https://github.com/yahyadogan72/iterative_facial_image_inpainting
공식 코드Iterative Facial Image Inpainting Based on an Encoder-Generator Architecture
https://github.com/yang-song/score_sde
공식 코드Score-Based Generative Modeling through Stochastic Differential Equations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.