https://github.com/smerdov/CS_GO_Sensors_Data_Analysis
공식 코드AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors
Papers with Code 아카이브
Image-to-Image Translation
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- 11
- 평가표
- 38
- 보존된 평가 기록
- 195
Image-to-Image Translation is a task in computer vision and machine learning where the goal is to learn a mapping between an input image and an output image, such that the output image can be used to perform a specific task, such as style transfer, data augmentation, or image restoration.
<span style="color:grey; opacity: 0.6">( Image credit: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/smerdov/eSports_Sensors_Dataset
공식 코드https://github.com/smerdov/smart_chair_client
공식 코드https://github.com/sony-si/MultiView-Img2Img
공식 코드https://github.com/sophiajw/HistAuGAN
공식 코드https://github.com/spaceml-org/expanding-sdo-capabilities
공식 코드Using U-Nets to Create High-Fidelity Virtual Observations of the Solar Corona
https://github.com/sqflow/sqflow
공식 코드Squeeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement
https://github.com/ssalazarcolores/Desmoking-laparoscopy-surgery-images-using-an-image-to-image-translation-guided-by-an-embedded-Dark-
공식 코드https://github.com/stefanleong/s2crnet
공식 코드Spatial-Separated Curve Rendering Network for Efficient and High-Resolution Image Harmonization
https://github.com/suhas-srinath/genselfdiff-his
공식 코드https://github.com/sulam-group/k-rcps
공식 코드How to Trust Your Diffusion Model: A Convex Optimization Approach to Conformal Risk Control
https://github.com/suxuann/ddib
공식 코드Dual Diffusion Implicit Bridges for Image-to-Image Translation
https://github.com/taesungp/contrastive-unpaired-translation
공식 코드Contrastive Learning for Unpaired Image-to-Image Translation
https://github.com/taki0112/UGATIT
공식 코드https://github.com/takuhirok/CP-GAN
공식 코드https://github.com/tamarott/ASAPNet
공식 코드Spatially-Adaptive Pixelwise Networks for Fast Image Translation
https://github.com/tan5o/anime2clothing
공식 코드Anime-to-Real Clothing: Cosplay Costume Generation via Image-to-Image Translation
https://github.com/tdchaitanya/MMTOD
공식 코드Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery
https://github.com/thmoa/tex2shape
공식 코드Tex2Shape: Detailed Full Human Body Geometry From a Single Image
https://github.com/tiagocortinhal/titan-next
공식 코드https://github.com/tobyclh/microglam
공식 코드https://github.com/tom919654/C3G-NeRF
공식 코드Class-Continuous Conditional Generative Neural Radiance Field
https://github.com/tychovdo/RevGAN
공식 코드Reversible GANs for Memory-efficient Image-to-Image Translation
https://github.com/tzt101/INADE
공식 코드Diverse Semantic Image Synthesis via Probability Distribution Modeling
https://github.com/ubc-vision/lsenerf
공식 코드LSE-NeRF: Learning Sensor Modeling Errors for Deblured Neural Radiance Fields with RGB-Event Stereo
https://github.com/user0407/CLUDA
공식 코드CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
https://github.com/uw-cmg/GAN-STEM-Conv2MultiSlice
공식 코드Exploring Generative Adversarial Networks for Image-to-Image Translation in STEM Simulation
https://github.com/valeoai/ADVENT
공식 코드ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/valeoai/DADA
공식 코드DADA: Depth-aware Domain Adaptation in Semantic Segmentation
https://github.com/vglsd/OpenSESAME
공식 코드SESAME: Semantic Editing of Scenes by Adding, Manipulating or Erasing Objects
https://github.com/vict0rsch/LiSS
공식 코드Towards Lifelong Self-Supervision For Unpaired Image-to-Image Translation
https://github.com/vikolss/DACS
공식 코드https://github.com/vios-s/csdisentanglement_metrics_library
공식 코드Measuring the Biases and Effectiveness of Content-Style Disentanglement
https://github.com/vishwa91/deepir
공식 코드https://github.com/visinf/da-sac
공식 코드Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
https://github.com/vita-group/font_completion_graph
공식 코드Font Completion and Manipulation by Cycling Between Multi-Modality Representations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.