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2D Classification

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

https://github.com/3P2S/arcface

Deep Residual Learning for Image Recognition

https://github.com/3a1b2c3/seeingSpace

Crowdsampling the Plenoptic Function

https://github.com/3dperceptionlab/therobotrix

UnrealROX: An eXtremely Photorealistic Virtual Reality Environment for Robotics Simulations and Synthetic Data Generation

https://github.com/3dperceptionlab/visual-wetlandbirds

MViTv2: Improved Multiscale Vision Transformers for Classification and Detection

https://github.com/3epochs/you-only-look-once

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

https://github.com/44aayush/Denoising-and-Enhancement

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/44aayush/MaskCycleGAN

MaskCycleGAN-VC: Learning Non-parallel Voice Conversion with Filling in Frames

https://github.com/497626895/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/4uiiurz1/pytorch-deform-conv-v2

Deformable ConvNets v2: More Deformable, Better Results

https://github.com/4uiiurz1/pytorch-nested-unet

UNet++: A Nested U-Net Architecture for Medical Image Segmentation

https://github.com/592595/TERA

Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning

https://github.com/593903762/center

Objects as Points

https://github.com/593903762/ttf

Training-Time-Friendly Network for Real-Time Object Detection

https://github.com/5m0k3/gwd-efficientdet-pytorch

Dynamic Scale Training for Object Detection

https://github.com/5m0k3/gwd-yolov5-pytorch

Dynamic Scale Training for Object Detection

https://github.com/6-dl/darknet_wpb

YOLOv3: An Incremental Improvement

https://github.com/6210612757/facerecognition

EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

https://github.com/6QHTSK/paper-database

Mind the Pad -- CNNs can Develop Blind Spots

https://github.com/7-B/yoco

Deep Photo Style Transfer

https://github.com/780753368/Deep-Feature-Perturbation

Diversified Arbitrary Style Transfer via Deep Feature Perturbation

https://github.com/812618101/TAL-Demo

Gaussian Temporal Awareness Networks for Action Localization

https://github.com/965694547/hybrid-system-of-frame-wise-model-and-sedt

Sound Event Detection Transformer: An Event-based End-to-End Model for Sound Event Detection

https://github.com/96jonesa/StyleGan2-Colab-Demo

Efficient Attention: Attention with Linear Complexities

https://github.com/97andrea97/CenterNet-PNPDet

Objects as Points

https://github.com/A-PolarBear/GoogLeNet

Going Deeper with Convolutions

https://github.com/A-Telfer/AugKey

RandAugment: Practical automated data augmentation with a reduced search space

https://github.com/A03ki/f-AnoGAN

https://github.com/A511-1103/Express_detection

Dense Label Encoding for Boundary Discontinuity Free Rotation Detection

https://github.com/ACheun9/Pytorch-implementation-of-Mobile-Former

Mobile-Former: Bridging MobileNet and Transformer

https://github.com/AD2605/Action-Recognition

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AGroupofProbiotocs/YOLO_V2_in_Tensorflow

YOLO9000: Better, Faster, Stronger

https://github.com/AI-Huang/SENet

Squeeze-and-Excitation Networks

https://github.com/AI-liu/Complex-YOLO

Complex-YOLO: Real-time 3D Object Detection on Point Clouds

https://github.com/AI-metrics/AI-metrics

Iterative Alternating Neural Attention for Machine Reading

https://github.com/AI-secure/Transferability-Reduced-Smooth-Ensemble

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/AI4Finance-LLC/DP-LSTM-Differential-Privacy-Stock-Prediction-Financial-News-NIPS-2019

DP-LSTM: Differential Privacy-inspired LSTM for Stock Prediction Using Financial News

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