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코드

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

https://github.com/maja601/pytorch-psetae

Satellite Image Time Series Classification with Pixel-Set Encoders and Temporal Self-Attention

https://github.com/makcedward/nlpaug

Character-level Convolutional Networks for Text Classification

https://github.com/makefile/DCR

Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

https://github.com/makeyourownmaker/mixup

Regularizing Neural Networks by Penalizing Confident Output Distributions

https://github.com/malan-project/malan

A Convolutional Transformation Network for Malware Classification

https://github.com/mamamot/Russian-ULMFit

Universal Language Model Fine-tuning for Text Classification

https://github.com/manankshastri/ART

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/manankshastri/Object-Detection

YOLO9000: Better, Faster, Stronger

https://github.com/mangushev/intent_slot

BERT for Joint Intent Classification and Slot Filling

https://github.com/mangye16/Unsupervised_Embedding_Learning

Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination

https://github.com/manhcuongk55/gender-classification-by-hand

Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network

https://github.com/manikandan-ravikiran/complex-assignments

Finding Black Cat in a Coal Cellar -- Keyphrase Extraction & Keyphrase-Rubric Relationship Classification from Complex Assignments

https://github.com/manish-vi/Human-Activity-Recognition

Understanding and Improving Deep Neural Network for Activity Recognition

https://github.com/manish-vi/Malware-Detection

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

https://github.com/manishravula/yolov3_tiny_pruned

YOLO9000: Better, Faster, Stronger

https://github.com/manu-vishwakarma/human-activity-detection

Understanding and Improving Deep Neural Network for Activity Recognition

https://github.com/manuel-rdz/c-tran

General Multi-label Image Classification with Transformers

https://github.com/manuelladron/Ambiguity-in-Computational-Creativity

Multimodal Word Sense Disambiguation in Creative Practice

https://github.com/manurare/Class-Inherent-Transformations

FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations

https://github.com/mapleleaf6/zode

The iNaturalist Species Classification and Detection Dataset

https://github.com/mappy/tf-faster-rcnn

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/maragraziani/iMIMIC-RCVs

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

https://github.com/marcovirgolin/UncertaintyEstimationInDeepNets

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/marfoq/unsupervised_translator

FastText.zip: Compressing text classification models

https://github.com/mari-linhares/tensorflow-maml

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

https://github.com/mariehane/prototypical-networks

Prototypical Networks for Few-shot Learning

https://github.com/mariostrbac/environmental-sound-classification

Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification

https://github.com/mariusarvinte/FATML-MiniProject

Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions

https://github.com/markson14/WheatDet

Is Heuristic Sampling Necessary in Training Deep Object Detectors?

https://github.com/marl/l3embedding

Look, Listen and Learn

https://github.com/marlcplhra/ShuffleNet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/marlin-codes/HTGN

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

https://github.com/marload/ConvNets-TensorFlow2

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/marsala97/Reproducibility-Project-Deep-Learning

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

https://github.com/martin-danelljan/Continuous-ConvOp

ATOM: Accurate Tracking by Overlap Maximization

https://github.com/martin-danelljan/ECO

ATOM: Accurate Tracking by Overlap Maximization

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