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하위 태스크

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평가표

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보존된 평가 기록

19

코드

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

https://github.com/MindCode-4/code-11/tree/main/ECG-arrhythmia-classification

ECG arrhythmia classification using a 2-D convolutional neural network

https://github.com/MindCode-4/code-11/tree/main/EWC-Overcoming-catastrophic-forgetting

Overcoming catastrophic forgetting in neural networks

https://github.com/MindCode-4/code-11/tree/main/Fixup-Initialization

Fixup Initialization: Residual Learning Without Normalization

https://github.com/MindCode-4/code-11/tree/main/fixed-classifier

Fix your classifier: the marginal value of training the last weight layer

https://github.com/MindCode-4/code-12/tree/main/Learning-to-Upsample

On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

https://github.com/MindCode-4/code-12/tree/main/from-softmax-to-sparsemax-a-sparse

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

https://github.com/MindCode-4/code-12/tree/main/local-relation-networks-for-image-recognition

Local Relation Networks for Image Recognition

https://github.com/MindCode-4/code-13/tree/main/res_mlp_ms

ResMLP: Feedforward networks for image classification with data-efficient training

https://github.com/MindCode-4/code-13/tree/main/vat

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

https://github.com/MindCode-4/code-3/tree/main/funnel

Funnelling: A New Ensemble Method for Heterogeneous Transfer Learning and its Application to Cross-Lingual Text Classification

https://github.com/MindCode-4/code-4/tree/main/roberta_prelayernorm

RoBERTa-wwm-ext Fine-Tuning for Chinese Text Classification

https://github.com/MindCode-4/code-5/tree/main/timesformer

Is Space-Time Attention All You Need for Video Understanding?

https://github.com/MindCode-4/code-6/tree/main/ECG-arrhythmia-classification

ECG arrhythmia classification using a 2-D convolutional neural network

https://github.com/MindCode-4/code-6/tree/main/EWC-Overcoming-catastrophic-forgetting

Overcoming catastrophic forgetting in neural networks

https://github.com/MindCode-4/code-7/tree/main/Fixup-Initialization

Fixup Initialization: Residual Learning Without Normalization

https://github.com/MindCode-4/code-7/tree/main/LGL-INR-master/LGL-INR-master

On the Learning Property of Logistic and Softmax Losses for Deep Neural Networks

https://github.com/MindCode-4/code-7/tree/main/fixed-classifier

Fix your classifier: the marginal value of training the last weight layer

https://github.com/MindCode-4/code-7/tree/main/from-softmax-to-sparsemax-a-sparse

From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification

https://github.com/MindCode-4/code-7/tree/main/local-relation-networks-for-image-recognition

Local Relation Networks for Image Recognition

https://github.com/MindCode-4/code-8/tree/main/multi-task-learning-using-uncertainty-to

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/MindCode-4/code-8/tree/main/res_mlp_ms

ResMLP: Feedforward networks for image classification with data-efficient training

https://github.com/MindCode-4/code-9/tree/main/vat

VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

https://github.com/MindSpore-paper-code-2/code2/tree/main/ReIDStrongBaseline

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/MindSpore-paper-code-2/code2/tree/main/ResidualAttentionNet

Residual Attention Network for Image Classification

https://github.com/MindSpore-paper-code-2/code2/tree/main/ntsnet

Learning to Navigate for Fine-grained Classification

https://github.com/MindSpore-paper-code-2/code2/tree/main/pnasnet

Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge

https://github.com/MindSpore-paper-code-2/code2/tree/main/pointtransformer

Point Transformer

https://github.com/MindSpore-paper-code-2/code2/tree/main/r2plus1d

Video Classification with Channel-Separated Convolutional Networks

https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_inceptionv2

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

https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet50

RegNet: Self-Regulated Network for Image Classification

https://github.com/MindSpore-paper-code-2/code399/tree/main/3D_DenseNet

DenseNet for Breast Tumor Classification in Mammographic Images

https://github.com/MindSpore-paper-code-2/code399/tree/main/ctcmodel

CTCModel: a Keras Model for Connectionist Temporal Classification

https://github.com/MindSpore-paper-code-2/code399/tree/main/dscnn

Dependency Sensitive Convolutional Neural Networks for Modeling Sentences and Documents

https://github.com/MindSpore-paper-code-2/code400/tree/main/MaskRCNN/maskrcnn_resnet50

RegNet: Self-Regulated Network for Image Classification

https://github.com/MindSpore-paper-code-3/code1/tree/main/AttentionCluster

Learnable pooling with Context Gating for video classification

https://github.com/MindSpore-paper-code-3/code10/tree/main/GridRCNN

An Implementation of Faster RCNN with Study for Region Sampling

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