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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/hy-zpg/DAGAN

Sampling Generative Networks

https://github.com/hyang0129/foodclassapp

Regularizing Neural Networks by Penalizing Confident Output Distributions

https://github.com/hycis/TensorGraph

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/hydrogo/rainnet

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

https://github.com/hyhmia/BlindMI

Machine Learning with Membership Privacy using Adversarial Regularization

https://github.com/hyungjik/TSC_practice

Deep learning for time series classification: a review

https://github.com/hyungrok-do/fair-glm-cvx

Penalizing Unfairness in Binary Classification

https://github.com/hyunwoongko/resnext-parallel

Aggregated Residual Transformations for Deep Neural Networks

https://github.com/i-am-g2/VehicleColorRecognition

Vehicle Color Recognition using Convolutional Neural Network

https://github.com/i7p9h9/swishnet

SwishNet: A Fast Convolutional Neural Network for Speech, Music and Noise Classification and Segmentation

https://github.com/iMoonLab/DHGNN

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/iMoonLab/MeshNet

MeshNet: Mesh Neural Network for 3D Shape Representation

https://github.com/iac-team/semseg

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

https://github.com/iamarjunchandra/LayoutLM-Form-Understanding---Sequence-Labeling

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

https://github.com/iamyuanchung/Autoregressive-Predictive-Coding

An Unsupervised Autoregressive Model for Speech Representation Learning

https://github.com/iamyuanchung/VQ-APC

An Unsupervised Autoregressive Model for Speech Representation Learning

https://github.com/ibabbar/Traffic-Sign-Classifier

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

https://github.com/ibayer/fastFM

fastFM: A Library for Factorization Machines

https://github.com/ibendrup/MulticolumnNetwork

Multicolumn Networks for Face Recognition

https://github.com/icanswim/cxr

ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

https://github.com/icey-zhang/ClassSR_paddle

ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

https://github.com/ichatnun/spatiospectral-densenet-rice-classification

Rice Classification Using Spatio-Spectral Deep Convolutional Neural Network

https://github.com/iclavera/meta-mb

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

https://github.com/iclr2019-anon/STAWM

A Biologically Inspired Visual Working Memory for Deep Networks

https://github.com/icoz69/DeepEMD

DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning

https://github.com/icps/tmc_ordinal_patterns

Leveraging the Self-Transition Probability of Ordinal Pattern Transition Graph for Transportation Mode Classification

https://github.com/icrto/xML

Evaluating the visualization of what a Deep Neural Network has learned

https://github.com/idc-dsi/Arapet

Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference

https://github.com/idearibosome/embracenet

EmbraceNet: A robust deep learning architecture for multimodal classification

https://github.com/idiap/fullgrad-saliency

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

https://github.com/ido90/roml-maml

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

https://github.com/ifding/simple-Infomax-pytorch

Learning deep representations by mutual information estimation and maximization

https://github.com/igorsterner/commute-opt-gnn

How Powerful are Graph Neural Networks?

https://github.com/ihsuy/Train-by-Reconnect

Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

https://github.com/iiyama-lab/PS-VAEs

Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift

https://github.com/ikarus-999/VDCNN_kor

Very Deep Convolutional Networks for Text Classification

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