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1 Image, 2*2 Stitching

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

https://github.com/syiin/ann_cancer_detection

Deep Residual Learning for Image Recognition

https://github.com/syiin/resnet_cancer_detection

Deep Residual Learning for Image Recognition

https://github.com/t04glovern/selfie2anime

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

https://github.com/t0930198/backup

Deep Residual Learning for Image Recognition

https://github.com/tabtoyou/kollava

Visual Instruction Tuning

https://github.com/tachitachi/GradientReversal

Unsupervised Domain Adaptation by Backpropagation

https://github.com/tadeephuy/GradientReversal

Unsupervised Domain Adaptation by Backpropagation

https://github.com/tahasamavati/Cycle-GAN-Tensorflow

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

https://github.com/taivu1998/GANime

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/taki0112/DRIT-Tensorflow

Diverse Image-to-Image Translation via Disentangled Representations

https://github.com/taki0112/FUNIT-Tensorflow

Few-Shot Unsupervised Image-to-Image Translation

https://github.com/taki0112/GDWCT-Tensorflow

Image-to-Image Translation via Group-wise Deep Whitening-and-Coloring Transformation

https://github.com/taki0112/MUNIT-Tensorflow

Multimodal Unsupervised Image-to-Image Translation

https://github.com/taki0112/SDIT-Tensorflow

SDIT: Scalable and Diverse Cross-domain Image Translation

https://github.com/taki0112/SPADE-Tensorflow

Semantic Image Synthesis with Spatially-Adaptive Normalization

https://github.com/taki0112/StarGAN-Tensorflow

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

https://github.com/taki0112/StarGAN_v2-Tensorflow

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/taki0112/UNIT-Tensorflow

Unsupervised Image-to-Image Translation Networks

https://github.com/takuhirok/NR-GAN

Class-Distinct and Class-Mutual Image Generation with GANs

https://github.com/tamlhp/dfd_benchmark

MesoNet: a Compact Facial Video Forgery Detection Network

https://github.com/tangji08/face-generator

Invertible Conditional GANs for image editing

https://github.com/taniokah/liarr2017

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/tanjeffreyz/deep-residual-learning

Deep Residual Learning for Image Recognition

https://github.com/tanpinquan/EE5934_2

Learning to Adapt Structured Output Space for Semantic Segmentation

https://github.com/taroogura/Person_reID_baseline_pytorch

Joint Discriminative and Generative Learning for Person Re-identification

https://github.com/tarujg/domain-adapt

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

https://github.com/tbullmann/imagesegmentation-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/tbullmann/imagetranslation-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/tcheung99/ResNet_MiniProject

Deep Residual Learning for Image Recognition

https://github.com/telecombcn-dl/2018-dlcv-team2

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/tensorflow/models

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/tensorflow/models/tree/master/research/audioset

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/tensorflow/models/tree/master/research/deeplab

Deep Residual Learning for Image Recognition

https://github.com/tensorflow/models/tree/master/research/gan

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/tensorflow/models/tree/master/research/slim

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/tensorpack/tensorpack

Deep Residual Learning for Image Recognition

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