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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/cl445/misestimation_mri_mdd

Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression

https://github.com/clam004/intro_continual_learning

Overcoming catastrophic forgetting in neural networks

https://github.com/clarivando/MAX-Audio-Classifier

CNN Architectures for Large-Scale Audio Classification

https://github.com/clavichord93/MENet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/clementchadebec/benchmark_VAE

Adversarial Autoencoders

https://github.com/clin366/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/cloudnine148/PHC_2nd_SPC

YOLO9000: Better, Faster, Stronger

https://github.com/clovaai/textual-kd-slu

vq-wav2vec: Self-Supervised Learning of Discrete Speech Representations

https://github.com/clovaai/vat-d

MixText: Linguistically-Informed Interpolation of Hidden Space for Semi-Supervised Text Classification

https://github.com/clrrrr/promp_plus

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

https://github.com/clvrai/representation-learning-by-learning-to-count

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/cmasch/cnn-text-classification

Convolutional Neural Networks for Sentence Classification

https://github.com/cnielly/prototypical-networks-omniglot

Prototypical Networks for Few-shot Learning

https://github.com/code-implementation1/Code1/tree/main/ecolite

ECO: Efficient Convolutional Network for Online Video Understanding

https://github.com/code-implementation1/Code2/tree/main/AdderNGD

ECO: Efficient Convolutional Network for Online Video Understanding

https://github.com/code-implementation1/Code4/tree/main/inception_resnet_v2

Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

https://github.com/code-implementation1/Code5/tree/main/MAML

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

https://github.com/code-implementation1/Code5/tree/main/meta-baseline

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

https://github.com/code-implementation1/Code6/tree/main/ntsnet

Learning to Navigate for Fine-grained Classification

https://github.com/code-implementation1/Code6/tree/main/pcb

A PCB Dataset for Defects Detection and Classification

https://github.com/code-implementation1/Code6/tree/main/pnasnet

Progressive Neural Architecture Search

https://github.com/code-implementation1/Code6/tree/main/pointtransformer

Point Transformer

https://github.com/code-implementation1/Code7/tree/main/ReIDStrongBaseline

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/code-implementation1/Code7/tree/main/ResidualAttentionNet

Residual Attention Network for Image Classification

https://github.com/code-implementation1/Code7/tree/main/resnet50_adv_pruning

RegNet: Self-Regulated Network for Image Classification

https://github.com/code-implementation1/Code7/tree/main/resnetv2_50_frn

RegNet: Self-Regulated Network for Image Classification

https://github.com/code-implementation1/Code8/tree/main/SiamFC

SiamFC++: Towards Robust and Accurate Visual Tracking with Target Estimation Guidelines

https://github.com/code-implementation1/Code8/tree/main/single_path_nas

Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours

https://github.com/code-implementation1/Code9/tree/main/ResNet

RegNet: Self-Regulated Network for Image Classification

https://github.com/code-implementation1/Code9/tree/main/sdne

The Effects of Randomness on the Stability of Node Embeddings

https://github.com/code-implementation1/Code9/tree/main/vgg

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/code2k13/nlphose

Bag of Tricks for Efficient Text Classification

https://github.com/code2k13/nlppipe

Bag of Tricks for Efficient Text Classification

https://github.com/codeKgu/text-gcn

Graph Convolutional Networks for Text Classification

https://github.com/codedecde/Recognizing-Textual-Entailment

Recurrent Batch Normalization

https://github.com/codelion/adaptive-classifier

Fast Meta-Learning for Adaptive Hierarchical Classifier Design

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