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2D Classification

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코드

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

https://github.com/augcog/dttdv1

Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset

https://github.com/augu0093/Voice-Conversion-Project

StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks

https://github.com/augu0093/Voice-Conversion-Project_StarGAN_Danspeech

StarGAN-VC: Non-parallel many-to-many voice conversion with star generative adversarial networks

https://github.com/aurelien-peden/Deep-Learning-paper-implementations

You Only Look Once: Unified, Real-Time Object Detection

https://github.com/auspicious3000/autopst

Global Rhythm Style Transfer Without Text Transcriptions

https://github.com/auspicious3000/autovc

AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss

https://github.com/autodeskailab/clip-forge

Zero-Shot Text-Guided Object Generation with Dream Fields

https://github.com/autoliuweijie/FastBERT

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

https://github.com/autonomousvision/navsim

TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving

https://github.com/avanetten/avanetten.github.io

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

https://github.com/avanetten/yolt

YOLO9000: Better, Faster, Stronger

https://github.com/avanitanna/robustfragmentvc

FragmentVC: Any-to-Any Voice Conversion by End-to-End Extracting and Fusing Fine-Grained Voice Fragments With Attention

https://github.com/avasalya/singleshot6Dpose

Squeezed Deep 6DoF Object Detection Using Knowledge Distillation

https://github.com/avellal14/AI-for-Art

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

https://github.com/avinash31d/paper-implementations/tree/main/mobile_vit

MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer

https://github.com/aviros/pointnet_totations

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/aviros/roatationPointnet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/avisekiit/wacv_2019

Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from Images

https://github.com/avm-debatr/bagganhq

PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection

https://github.com/avm-debatr/ganecdotes

PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection

https://github.com/avocardio/resnet_vs_convnext

Deep Residual Learning for Image Recognition

https://github.com/avshalomc/subtract

Style Transfer Through Back-Translation

https://github.com/awant/arae

Adversarially Regularized Autoencoders

https://github.com/aws-samples/amazon-sagemaker-edge-defect-detection-computer-vision

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/awsaf49/gcvit-tf

Global Context Vision Transformers

https://github.com/awslabs/dynamic-training-with-apache-mxnet-on-aws

Dynamic Mini-batch SGD for Elastic Distributed Training: Learning in the Limbo of Resources

https://github.com/awslabs/hypergraph-tabular-lm

TABBIE: Pretrained Representations of Tabular Data

https://github.com/axmoyal/Steganalysis-with-CNN

Deep Residual Learning for Image Recognition

https://github.com/ayanc/ndeblur

A Neural Approach to Blind Motion Deblurring

https://github.com/ayanglab/swinganmr

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/ayanglab/swinmr

Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

https://github.com/ayoolaolafenwa/PixelLib

https://github.com/ayoolaolafenwa/orangelib

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/ayoub-sudo/Attention-Unet-web-app

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/ayoungkang/pytorch_Yolov4_lane-detection

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/ayoungkang/yolov4

YOLOv4: Optimal Speed and Accuracy of Object Detection

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