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코드

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

https://github.com/PurvaChiniya/Effects-of-image-quality-on-deep-nueral-network-paper-code

Understanding How Image Quality Affects Deep Neural Networks

https://github.com/Puzer/stylegan-encoder

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Pxtri2156/AdelaiDet_v2

SOLO: Segmenting Objects by Locations

https://github.com/PythonZhengbinChen/tf-faster-rcnn-cpu

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/QData/DeepLearning4-ProteinSequenceProcessing

MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

https://github.com/QData/DeepMotif

Deep Motif Dashboard: Visualizing and Understanding Genomic Sequences Using Deep Neural Networks

https://github.com/QData/DeepProtein

MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction

https://github.com/QUVA-Lab/e2cnn

Steerable CNNs

https://github.com/Qengineering/ARMnn_Quantize_RPi_32-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/MobileNetV1_SSD_OpenCV_Caffe

YOLO9000: Better, Faster, Stronger

https://github.com/Qengineering/TensorFlow_Lite_Classification_Jetson-Nano

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_32-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_64-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_Classification_RPi_zero

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_RPi_32-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/TensorFlow_Lite_RPi_64-bits

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/Qengineering/YoloV2-ncnn-Jetson-Nano

YOLO9000: Better, Faster, Stronger

https://github.com/Qengineering/YoloV2-ncnn-Raspberry-Pi-4

YOLO9000: Better, Faster, Stronger

https://github.com/QiaoLiuHit/HSSNet

Hierarchical Spatial-aware Siamese Network for Thermal Infrared Object Tracking

https://github.com/Qinhj07/ATOMCode

Acquisition of Localization Confidence for Accurate Object Detection

https://github.com/QustKcz/relational-GCN

Modeling Relational Data with Graph Convolutional Networks

https://github.com/RAJAT--PALIWAL/research_AI

A Brief Survey of Text Mining: Classification, Clustering and Extraction Techniques

https://github.com/RF5/CurveNet

Walk in the Cloud: Learning Curves for Point Clouds Shape Analysis

https://github.com/RPraneetha/MoCo-FAIR

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/RRisto/quickthoughts

An efficient framework for learning sentence representations

https://github.com/RSMung/DefenseGAN-pytorch

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

https://github.com/RaRe-Technologies/gensim-data

Bag of Tricks for Efficient Text Classification

https://github.com/RadicalAcronym/URSA

URSA: A Neural Network for Unordered Point Clouds Using Constellations

https://github.com/RaffaeleGalliera/pytorch-cnn-text-classification

Convolutional Neural Networks for Sentence Classification

https://github.com/Raghuvar/pneumonia_detection_using_X-rays

Thresholding Classifiers to Maximize F1 Score

https://github.com/RajasSU/Twitter-Sentiment-Analysis-using-ULMFiT

Universal Language Model Fine-tuning for Text Classification

https://github.com/RajkumarPreetham/Road-scene-understanding

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/Rakeshpavan333/oct_dil

Multi-Scale Context Aggregation by Dilated Convolutions

https://github.com/Raman-Raje/Microsoft-Malware-Detecion

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

https://github.com/Raman-Raje/Microsoft-Malware-Detection

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

https://github.com/RangiLyu/mmdetection_test

SSH: Single Stage Headless Face Detector

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