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General Classification

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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/LouisFoucard/StereoConvNet

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

https://github.com/LourisXu/rfs

Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need?

https://github.com/Luab/Explanation_by_Progressive_Exaggeration_Pytorch

Explanation by Progressive Exaggeration

https://github.com/LuanAdemi/VisualGo

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/LucaMozzo/SpikingNeuralNetwork

Training Probabilistic Spiking Neural Networks with First-to-spike Decoding

https://github.com/LucasElbert/GradCam

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/LukasHedegaard/co3d

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

https://github.com/Lukasz-G/Hydra

Simple Recurrent Units for Highly Parallelizable Recurrence

https://github.com/LukeBolly/tf-tspn/blob/master/models/fspool.py

FSPool: Learning Set Representations with Featurewise Sort Pooling

https://github.com/Lukeasargen/targetClassifier

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

https://github.com/Lukeli0425/Fire-Detection

BoWFire: Detection of Fire in Still Images by Integrating Pixel Color and Texture Analysis

https://github.com/LuongPhan/UIT-ViSFD

SA2SL: From Aspect-Based Sentiment Analysis to Social Listening System for Business Intelligence

https://github.com/LurreMcFly/ERP-PREDICTION-CONTEST

Imaging Time-Series to Improve Classification and Imputation

https://github.com/Lynda-Starkus/Abnormal_ECG_Myocardial_infraction_cnn

ECG Heartbeat Classification: A Deep Transferable Representation

https://github.com/M155K4R4/fastText

Bag of Tricks for Efficient Text Classification

https://github.com/MALHARULHAS/A-Country_level-location-classification-system-for-twitter-tweets-from-the-whole-world

Towards Real-Time, Country-Level Location Classification of Worldwide Tweets

https://github.com/MARMOTatZJU/ZSLPR-TIANCHI

Zero-Shot Visual Recognition using Semantics-Preserving Adversarial Embedding Networks

https://github.com/MDSKUL/MasterProject

VideoBERT: A Joint Model for Video and Language Representation Learning

https://github.com/MIC-DKFZ/ACDC2017

Automatic Cardiac Disease Assessment on cine-MRI via Time-Series Segmentation and Domain Specific Features

https://github.com/MIC-DKFZ/TractSeg

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

https://github.com/MINGUKKANG/Adversarial-AutoEncoder

Adversarial Autoencoders

https://github.com/MJ-Jang/Paraphrase-Thought

Paraphrase Thought: Sentence Embedding Module Imitating Human Language Recognition

https://github.com/MJahangeerQureshi/Text-Classification

Universal Language Model Fine-tuning for Text Classification

https://github.com/ML-Aksh/COV-AID

Efficient and Visualizable Convolutional Neural Networks for COVID-19 Classification Using Chest CT

https://github.com/ML-Bee/associative-alignment-fs

Associative Alignment for Few-shot Image Classification

https://github.com/MLDroid/graph2vec_tf

graph2vec: Learning Distributed Representations of Graphs

https://github.com/MLEnthusiast/MHCLN

AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/crossvit

CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/googlenet

Going Deeper with Convolutions

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/inception_v4

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

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/resnext

Aggregated Residual Transformations for Deep Neural Networks

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/shufflenet_v1

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/MS-Mind/MS-Code-03

AdaNet: Adaptive Structural Learning of Artificial Neural Networks

https://github.com/MS-Mind/MS-Code-06/tree/main/ntsnet

Learning to Navigate for Fine-grained Classification

https://github.com/MS-Mind/MS-Code-06/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/MS-Mind/MS-Code-06/tree/main/pointtransformer

Point Transformer

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