Papers with Code 아카이브

1 Image, 2*2 Stitching

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

2

평가표

0

보존된 평가 기록

0

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/KatherineBaq/NMA-CN2021-fMRI_vs_DNNs

Deep Residual Learning for Image Recognition

https://github.com/KbeautyHair/BaselineModel

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/KeiraZhao/MDAN

Unsupervised Domain Adaptation by Backpropagation

https://github.com/KellerJordan/ResNet-PyTorch-CIFAR10

Deep Residual Learning for Image Recognition

https://github.com/KellyHwong/rethinking_generalization

Deep Residual Learning for Image Recognition

https://github.com/King-Otaku/Emotion_Recognition_DNN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Kokonut133/MagicDraw

Semantic Image Synthesis with Spatially-Adaptive Normalization

https://github.com/Kokonut133/frame2frame

Semantic Image Synthesis with Spatially-Adaptive Normalization

https://github.com/KookHoiKim/AdaptSegNet

Learning to Adapt Structured Output Space for Semantic Segmentation

https://github.com/Kprerak-iisc/data-science

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/KrishnaswamyLab/travelgan

TraVeLGAN: Image-to-image Translation by Transformation Vector Learning

https://github.com/LKLQQ/StarGAN

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

https://github.com/LKLQQ/ssc_resnet50

Deep Residual Learning for Image Recognition

https://github.com/LLaVA-VL/LLaVA-NeXT

Visual Instruction Tuning

https://github.com/Leinadh/avatar-image-generator

XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

https://github.com/Lester1711/DSI15_Capstone

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/LiYuhangUSTC/Sketch2Face

DeepFacePencil: Creating Face Images from Freehand Sketches

https://github.com/Little-Frog-233/imagecluster

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/LiuNull/pix2pix_Liu

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/LoganOneal/neuralpaint-server

Semantic Image Synthesis with Spatially-Adaptive Normalization

https://github.com/Lornatang/CoGAN-PyTorch

Coupled Generative Adversarial Networks

https://github.com/Lornatang/PyTorch-CycleGAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/Lornatang/ResNet-PyTorch

Deep Residual Learning for Image Recognition

https://github.com/Lornatang/tensorflow-vgg

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Lotayou/CoCosNet

Cross-domain Correspondence Learning for Exemplar-based Image Translation

https://github.com/Lotayou/everybody_dance_now_pytorch

Everybody Dance Now

https://github.com/LouisRouss/Diffusion-Based-Model-for-Colorization

Palette: Image-to-Image Diffusion Models

https://github.com/LuigiRussoDev/Covid19Detection

Deep Residual Learning for Image Recognition

https://github.com/LuigiRussoDev/ResNets

Deep Residual Learning for Image Recognition

https://github.com/LynnHo/AttGAN-Tensorflow

Invertible Conditional GANs for image editing

https://github.com/LynnHo/CycleGAN-Tensorflow-2

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/LynnHo/CycleGAN-Tensorflow-PyTorch-Simple

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/MASILab/SynSeg-Net

Adversarial Synthesis Learning Enables Segmentation Without Target Modality Ground Truth

https://github.com/MNaseerSubhani/LSE

Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnet

Deep Residual Learning for Image Recognition

https://github.com/MS-Mind/MS-Code-06/tree/main/Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.