https://github.com/ZhangGongjie/CAD-Net
CAD-Net: A Context-Aware Detection Network for Objects in Remote Sensing Imagery
Papers with Code 아카이브
2D Classification
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ZhangGongjie/CycleGAN-Pytorch
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
https://github.com/ZhangJiajun1995/cs231n-camp
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
https://github.com/Zhangyongtao123/maskrcnn_benchmark
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
https://github.com/ZhaoChuyang/trans-person-search
https://github.com/ZhaoHengrun/EDVR
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
https://github.com/ZhaojieL/HTE-data
Decoupling Speaker-Independent Emotions for Voice Conversion Via Source-Filter Networks
https://github.com/ZhenJie-Zhang/darknet_shoe
https://github.com/Zhengqi-Wu/Unrolled-optimization-with-deep-priors
https://github.com/ZhichengHuang/Food-Project
RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free
https://github.com/ZhihaoZhu/PointNet-Implementation-Tensorflow
VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection
https://github.com/ZhiqiWang12/try_darknet
https://github.com/Zhiwei-Z/PrompLimitTest
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Zhiwei-Z/SeqPromp
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/Zhiwei-Z/prompzzw
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/ZhixinLai/3D-detection-with-monocular-RGB-image
https://github.com/ZhouKai90/face-detection-ssd-mxnet
https://github.com/Zhuoranbupt/ECE285fa19
https://github.com/Zhz1997/Singing-voice-speration-with-U-Net
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/Zoky-2020/Set-level_Guidance_Attack
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/Zoushuang86/quiz_w8
https://github.com/ZyoungXu/MoSt-DSA
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ZyphonGT/TFLite-Frontier-Project-UPH
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/Zzh-tju/DIoU-SSD-pytorch
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
https://github.com/Zzh-tju/DIoU-darknet
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
https://github.com/Zzh-tju/DIoU-pytorch-detectron
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
https://github.com/Zzh-tju/ultralytics-YOLOv3-Cluster-NMS
https://github.com/a-martyn/unet
U-Net: Convolutional Networks for Biomedical Image Segmentation
https://github.com/a-nau/Plane-Segmentation-Refinement
Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge Detection
https://github.com/a-nau/easy-image-scraping
Scrape, Cut, Paste and Learn: Automated Dataset Generation Applied to Parcel Logistics
https://github.com/a-nau/image-selection-and-cnn-training
Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection
https://github.com/a1302z/OktoberfestFoodDataset
https://github.com/a736875071/clip-vit-large-patch14
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/aHapBean/PCP-MAE
https://github.com/aRI0U/music-source-separation
Improving DNN-based Music Source Separation using Phase Features
https://github.com/aaboys/mobileformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.