Papers with Code 아카이브
General Classification
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Algorithms trying to solve the general task of classification.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/2023-MindSpore-4/Code14/tree/main/resnet50_adv_pruning
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/2023-MindSpore-4/Code14/tree/main/resnext152_64x4d
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/2023-MindSpore-4/Code14/tree/main/simclr
https://github.com/2023-MindSpore-4/Code3/tree/main/densenet
https://github.com/2023-MindSpore-4/Code3/tree/main/dpn
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
https://github.com/2023-MindSpore-4/Code6/tree/main/ReIDStrongBaseline
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2023-MindSpore-4/Code6/tree/main/ResidualAttentionNet
https://github.com/2023-MindSpore-4/Code7/tree/main/single_path_nas
Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours
https://github.com/2023-MindSpore-4/Code7/tree/main/slowfast
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_inceptionv2
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/2023-MindSpore-4/Code8/tree/main/3D_DenseNet
DenseNet: Implementing Efficient ConvNet Descriptor Pyramids
https://github.com/2023-MindSpore-4/Code8/tree/main/C3D
R-C3D: Region Convolutional 3D Network for Temporal Activity Detection
https://github.com/2023-MindSpore-4/Code8/tree/main/CBAM
https://github.com/2023-MindSpore-4/Code9/tree/main/Inception
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/2023-MindSpore-4/Code9/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2023-MindSpore-4/Code9/tree/main/VGG
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/2024-MindSpore-1/Code4/tree/main/ShuffleNet
ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
https://github.com/2024-MindSpore-1/Code5/tree/main/3D_DenseNet
DenseNet for Breast Tumor Classification in Mammographic Images
https://github.com/2024-MindSpore-1/Code6/tree/main/AttentionCluster
Attention Clusters: Purely Attention Based Local Feature Integration for Video Classification
https://github.com/2024-MindSpore-1/Code7/tree/main/CBAM
https://github.com/24yearsoldstudent/individual-nlu
https://github.com/30Aniket/Graph-Convolutional-Networks-GCNs
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/32shivang/Blind-Eye
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
https://github.com/3480430977/ACDNE
Adversarial Deep Network Embedding for Cross-network Node Classification
https://github.com/360jinrong/GBST
https://github.com/3D-ResNets-Reproduction/Reference-Project
Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?
https://github.com/3dimaging/DeepLearningCamelyon
https://github.com/3dperceptionlab/visual-wetlandbirds
Rethinking Spatiotemporal Feature Learning: Speed-Accuracy Trade-offs in Video Classification
https://github.com/3epochs/you-only-look-once
https://github.com/497626895/darknet
https://github.com/53X/CogniNET
Deep Learning Human Mind for Automated Visual Classification
https://github.com/571502680/robot-grasp-detection
Real-Time Grasp Detection Using Convolutional Neural Networks
https://github.com/592595/TERA
Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoders
https://github.com/632652101/VisualizeCNN-Pd
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/812618101/TAL-Demo
Fast Learning of Temporal Action Proposal via Dense Boundary Generator
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.