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General Classification

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코드

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

https://github.com/olivesgatech/man-recon

A Machine Learning Benchmark for Facies Classification

https://github.com/oliviayong/MUeconProgramming-ML

Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale

https://github.com/omacshane/basicbayesDL

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

https://github.com/omarfoq/unsupervised_translator

FastText.zip: Compressing text classification models

https://github.com/omglu93/energy_consumption_eda_prediction

XGBoost: A Scalable Tree Boosting System

https://github.com/ondrejba/tf_calibrate

On Calibration of Modern Neural Networks

https://github.com/onehaitao/CR-CNN-relation-extraction

Classifying Relations by Ranking with Convolutional Neural Networks

https://github.com/onehaitao/R-BERT-relation-extraction

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

https://github.com/ongunuzaymacar/comparatively-finetuning-bert

How to Fine-Tune BERT for Text Classification?

https://github.com/oorrppp2/darknet-pose

YOLO9000: Better, Faster, Stronger

https://github.com/opallab/icml-21-graph-conv

Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization

https://github.com/open-edge-platform/geti

Aggregated Residual Transformations for Deep Neural Networks

https://github.com/open-edge-platform/training_extensions

Aggregated Residual Transformations for Deep Neural Networks

https://github.com/open-mmlab/mmaction2

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

https://github.com/open-mmlab/mmclassification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/open-mmlab/mmdetection

SSH: Single Stage Headless Face Detector

https://github.com/open-mmlab/mmpose

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/open-mmlab/mmrazor

DARTS: Differentiable Architecture Search

https://github.com/open-mmlab/mmselfsup

Unsupervised Representation Learning by Predicting Image Rotations

https://github.com/openai/ebm_code_release

Implicit Generation and Generalization in Energy-Based Models

https://github.com/openml/openml-aslib

OpenML Benchmarking Suites

https://github.com/opringle/VDCNN-for-text-classification

Very Deep Convolutional Networks for Text Classification

https://github.com/optimization-ai/iccv2021_deepauc

Large-scale Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification

https://github.com/optimization-for-data-driven-science/dair

Theoretically Principled Trade-off between Robustness and Accuracy

https://github.com/oraby8/TextDataAug

EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks

https://github.com/orientino/dum-components

On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty

https://github.com/orion-wyc/gaga

Bag of Tricks for Node Classification with Graph Neural Networks

https://github.com/orobix/Prototypical-Networks-for-Few-shot-Learning-PyTorch

Prototypical Networks for Few-shot Learning

https://github.com/oscarknagg/few-shot

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

https://github.com/osman-haider/Decision-Tree-Stream

Decision Stream: Cultivating Deep Decision Trees

https://github.com/osmr/imgclsmob

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/osnandhu/DS5500_PCG_Classification

An Ensemble of Transfer, Semi-supervised and Supervised Learning Methods for Pathological Heart Sound Classification

https://github.com/osofr/xgboost

XGBoost: A Scalable Tree Boosting System

https://github.com/oxcsaml2019/multitask-learning

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

https://github.com/p-lambda/verified_calibration

On Calibration of Modern Neural Networks

https://github.com/pa56/SegNet_on_Cityscapes

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

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