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General Classification

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하위 태스크

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평가표

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보존된 평가 기록

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코드

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

https://github.com/dizcza/EmbedderSDR

MINE: Mutual Information Neural Estimation

https://github.com/dizcza/pytorch-mighty

MINE: Mutual Information Neural Estimation

https://github.com/djcustard/GestureGAN

Going Deeper with Convolutions

https://github.com/djwhsdj/vw-sdk

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/dkalpakchi/ReproducingSCAPytorch

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/dkman94/FastrApp-Android

Learning to detect dysarthria from raw speech

https://github.com/dlmacedo/isotropic-maximization-loss-and-entropic-score

Entropic Out-of-Distribution Detection: Seamless Detection of Unknown Examples

https://github.com/dlpbc/keras-kinetics-i3d

Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset

https://github.com/dmbernaal/Daedalus

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

https://github.com/dmcv-ecnu/MindSpore_ModelZoo/tree/main/few-shot-meta-baseline_mindspore

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning

https://github.com/dmitrykazhdan/MEME-RNN-XAI

Multitask learning and benchmarking with clinical time series data

https://github.com/dmizr/phuber

Learning with Symmetric Label Noise: The Importance of Being Unhinged

https://github.com/dmlc/dgl/tree/master/examples/mxnet/appnp

Predict then Propagate: Graph Neural Networks meet Personalized PageRank

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/gin

How Powerful are Graph Neural Networks?

https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/mxnet/tree_lstm

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/appnp

Predict then Propagate: Graph Neural Networks meet Personalized PageRank

https://github.com/dmlc/dgl/tree/master/examples/pytorch/arma

Graph Neural Networks with convolutional ARMA filters

https://github.com/dmlc/dgl/tree/master/examples/pytorch/compGCN

Composition-based Multi-Relational Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/dgi

Deep Graph Infomax

https://github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool

Hierarchical Graph Representation Learning with Differentiable Pooling

https://github.com/dmlc/dgl/tree/master/examples/pytorch/evolveGCN

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gin

How Powerful are Graph Neural Networks?

https://github.com/dmlc/dgl/tree/master/examples/pytorch/gxn

Graph Cross Networks with Vertex Infomax Pooling

https://github.com/dmlc/dgl/tree/master/examples/pytorch/mvgrl

Contrastive Multi-View Representation Learning on Graphs

https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn-hetero

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/pytorch/seal

Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning

https://github.com/dmlc/dgl/tree/master/examples/pytorch/tree_lstm

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/dgi

Deep Graph Infomax

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/dmlc/dgl/tree/master/examples/tensorflow/rgcn

Modeling Relational Data with Graph Convolutional Networks

https://github.com/dmlc/gluon-nlp

Convolutional Neural Networks for Sentence Classification

https://github.com/dnddnjs/pytorch-cifar10

Deep Pyramidal Residual Networks

https://github.com/dodoproptit99/deep-speaker

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

https://github.com/domainadaptation/salad

Domain-Adversarial Training of Neural Networks

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