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코드

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

https://github.com/mhd-medfa/NoisyStudent-Based-Object-Recognition

Self-training with Noisy Student improves ImageNet classification

https://github.com/mhjabreel/CharCNN

Character-level Convolutional Networks for Text Classification

https://github.com/mhjabreel/CharCnn_Keras

Character-level Convolutional Networks for Text Classification

https://github.com/mhw32/prototransformer-public

Induction Networks for Few-Shot Text Classification

https://github.com/mhwong2007/Deep-Packet

Deep Packet: A Novel Approach For Encrypted Traffic Classification Using Deep Learning

https://github.com/michael-aloys/awesome-weak-supervision

Image Classification with Deep Learning in the Presence of Noisy Labels: A Survey

https://github.com/michael13162/DoodleGAN

Sampling Generative Networks

https://github.com/michaelhoarau/mtf-deep-dive

Encoding Temporal Markov Dynamics in Graph for Visualizing and Mining Time Series

https://github.com/michhar/azureml-keras-yolov3-custom

YOLO9000: Better, Faster, Stronger

https://github.com/michiel-j/DANN-two-moons-PyTorch

Domain-Adversarial Training of Neural Networks

https://github.com/mickeystroller/R-BERT

Enriching Pre-trained Language Model with Entity Information for Relation Classification

https://github.com/microsoft/Swin-Transformer

Local Relation Networks for Image Recognition

https://github.com/microsoft/UDA

MetaAlign: Coordinating Domain Alignment and Classification for Unsupervised Domain Adaptation

https://github.com/microsoft/computervision-recipes/blob/master/scenarios/similarity/02_state_of_the_art.ipynb

Classification is a Strong Baseline for Deep Metric Learning

https://github.com/microsoft/fb.resnet.torch

Some Improvements on Deep Convolutional Neural Network Based Image Classification

https://github.com/microsoft/metric-transfer.pytorch

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/microsoft/ticl

TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second

https://github.com/microsoft/unilm/tree/master/layoutlm

Cutting the Error by Half: Investigation of Very Deep CNN and Advanced Training Strategies for Document Image Classification

https://github.com/midobal/OpenNMT-py

Simple Recurrent Units for Highly Parallelizable Recurrence

https://github.com/mihirp1/Deep_Learning_Project_CPSC8810

Visualizing and Understanding Convolutional Networks

https://github.com/mikehuisman/revisiting-learned-optimizers

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

https://github.com/mikekestemont/deepscript

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mikel-brostrom/Deep_Spammer_Detection_GCN

SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels

https://github.com/mikuhatsune/dap

DAP: Detection-Aware Pre-training with Weak Supervision

https://github.com/mil-tokyo/bc_learning_sound

Between-class Learning for Image Classification

https://github.com/milliondegree/semantic-segmentation-tensorflow

Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/shufflenetv1.py

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/mindlab-ai/mindcv/blob/main/mindcv/models/vgg.py

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mindorii/kws

An End-to-End Architecture for Keyword Spotting and Voice Activity Detection

https://github.com/mindscan-de/FluentGenesis-Classifier

SCC: Automatic Classification of Code Snippets

https://github.com/mindspore-ai/contrib/blob/master/application/EWC-Overcoming-catastrophic-forgetting/demo.ipynb

Overcoming catastrophic forgetting in neural networks

https://github.com/mindspore-ai/models/blob/master/official/cv/shufflenetv1

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/mindspore-ai/models/blob/master/research/cv/SPPNet

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://github.com/mindspore-ai/models/tree/master/official/cv/googlenet

Going Deeper with Convolutions

https://github.com/mindspore-ai/models/tree/master/official/cv/inceptionv4

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

https://github.com/mindspore-ai/models/tree/master/official/cv/resnext

Aggregated Residual Transformations for Deep Neural Networks

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