https://github.com/hayko1995/JHU-CROWD-Detection
Focal Inverse Distance Transform Maps for Crowd Localization
Papers with Code 아카이브
SSIM
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ideal-iqa/haarpsi-pytorch
A study on the adequacy of common IQA measures for medical images
https://github.com/inyong37/Vision
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images
https://github.com/ipc-lab/DWSIC
https://github.com/ipc-lab/NDIC
https://github.com/ipc-lab/ndic-cam
https://github.com/isaacschaal/SG_training
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/jiang-du/Perceptual-CS
Fully Convolutional Measurement Network for Compressive Sensing Image Reconstruction
https://github.com/jooyae/NVIDIA_STYLEGAN
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/jooyoungleeetri/scr
https://github.com/jorge-pessoa/pytorch-gdn
https://github.com/junhyeog/EDS
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
https://github.com/jyhmiinlin/pynufft
https://github.com/kamkyu94/BitNet
https://github.com/khammernik/sigmanet
$Σ$-net: Ensembled Iterative Deep Neural Networks for Accelerated Parallel MR Image Reconstruction
https://github.com/khurram702/StyleBasedGAN
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/kingcong/csd
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/klieberman/ood_nic
https://github.com/kozistr/gan-metrics
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
https://github.com/kushalpatil1997/text_to_image_synthesis
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial Network
https://github.com/lartpang/HDFNet
Multi-modal Bifurcated Network for Depth Guided Image Relighting
https://github.com/leonmakise/NBNet_Pytorch
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
https://github.com/liujiaheng/iclr_17_compression
https://github.com/makinyilmaz/lhbdc
End-to-End Rate-Distortion Optimized Learned Hierarchical Bi-Directional Video Compression
https://github.com/mandt-lab/improving-inference-for-neural-image-compression
https://github.com/marcovaldong/isgan
End-to-end Trained CNN Encode-Decoder Networks for Image Steganography
https://github.com/mareikethies/geometry_gradients_ct
Gradient-Based Geometry Learning for Fan-Beam CT Reconstruction
https://github.com/mducducd/Shadow-Removal
https://github.com/mducducd/ghost-free-shadow-removal
https://github.com/megengine/nbnet
NBNet: Noise Basis Learning for Image Denoising with Subspace Projection
https://github.com/michaelkoelle/quantum-diffusion
https://github.com/microsoft/dcvc
https://github.com/mindspore-ai/contrib/tree/master/papers/CSD
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/mindspore-ai/models/tree/master/research/cv/csd
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/mmSir/GainedVAE
Asymmetric Gained Deep Image Compression With Continuous Rate Adaptation
https://github.com/nazmul-karim170/FIP-Fisher-Backdoor-Removal
SPI-GAN: Towards Single-Pixel Imaging through Generative Adversarial Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.