Papers with Code 아카이브

1 Image, 2*2 Stitching

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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보존된 평가 기록

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코드

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

https://github.com/zhenxingjian/partial_distance_correlation

Measuring the Biases and Effectiveness of Content-Style Disentanglement

https://github.com/zhenxuan00/graphical-gan

Adversarially Learned Inference

https://github.com/zhouZhouYUI/UGATIT_paddle

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

https://github.com/zhuangpeiyu/reloc

Practical Blind Image Denoising via Swin-Conv-UNet and Data Synthesis

https://github.com/zhuo931077127/CycleGAN_practice

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

https://github.com/zjZSTU/ResNet

Deep Residual Learning for Image Recognition

https://github.com/zjsong/CDNet

Invertible Conditional GANs for image editing

https://github.com/zjsong/sspl

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/znxlwm/pytorch-pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/zqwhu/SegDAwithBoundary

Learning to Adapt Structured Output Space for Semantic Segmentation

https://github.com/zsano1/MEnet-FCN-wild

FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation

https://github.com/zuobinxiong/pix2pix-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/zzz2010/starganv2_paddle

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://gitlab.com/birder/birder

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://gitlab.com/nct_tso_public/surgical-video-sim2real

Multimodal Unsupervised Image-to-Image Translation

https://gitlab.com/williamsdaniel888/valma

Very Deep Convolutional Networks for Large-Scale Image Recognition

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