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General Classification

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평가표

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보존된 평가 기록

19

코드

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

https://github.com/ShichengChen/Domain-Adversarial-Training-of-Neural-Networks

Domain-Adversarial Training of Neural Networks

https://github.com/ShichengChen/WaveNetSeparateAudio

Domain-Adversarial Training of Neural Networks

https://github.com/ShigemichiMatsuzaki/MSPL

ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network

https://github.com/ShihuiYin/BASNN

Algorithm and Hardware Design of Discrete-Time Spiking Neural Networks Based on Back Propagation with Binary Activations

https://github.com/ShindongLee/Sentence_Classifier_CNN

Convolutional Neural Networks for Sentence Classification

https://github.com/Shito0907/3D_resnet

Would Mega-scale Datasets Further Enhance Spatiotemporal 3D CNNs?

https://github.com/ShivamRajSharma/Transformer-Architectures-From-Scratch

Visual Transformers: Token-based Image Representation and Processing for Computer Vision

https://github.com/Shivamt99/Capstone_Project

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/ShobhitMaheshwari/sign-language1

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

https://github.com/Shraddha2013/customyolo

YOLO9000: Better, Faster, Stronger

https://github.com/Shraddha2013/darknett

YOLO9000: Better, Faster, Stronger

https://github.com/Shraddha2013/darknetyol

YOLO9000: Better, Faster, Stronger

https://github.com/Shraddha2013/darknetyolo

YOLO9000: Better, Faster, Stronger

https://github.com/Shraddha2013/darknetyolo3

YOLO9000: Better, Faster, Stronger

https://github.com/Shraddha2013/mydarknetyolo

YOLO9000: Better, Faster, Stronger

https://github.com/Shraddha2013/myfileyolo

YOLO9000: Better, Faster, Stronger

https://github.com/ShreyaKhare/imdb_fasttext

Bag of Tricks for Efficient Text Classification

https://github.com/ShuanDeMorian/Project_AT_News_Classification

Joint Embedding of Words and Labels for Text Classification

https://github.com/Shubhambindal2017/Unoffical-Implementation-of-GRAD-CAM-with-Sanity-Checks

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

https://github.com/ShubhayanS/Multiclass-Diabetic-Retinopathy-Detection

Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset

https://github.com/Shujian2015/meta-embedding-paper-list

Think Globally, Embed Locally --- Locally Linear Meta-embedding of Words

https://github.com/ShutoAraki/EverybodyDanceNow-Temporal-FaceGAN

Generative Adversarial Networks: An Overview

https://github.com/ShuvenduBikash/transformer_spelling_corrector

A Structured Self-attentive Sentence Embedding

https://github.com/SiHaoShen/Convolutional-Neural-Networks

YOLO9000: Better, Faster, Stronger

https://github.com/SilvioGiancola/maskrcnn-benchmark

Is Heuristic Sampling Necessary in Training Deep Object Detectors?

https://github.com/SinclairHudson/DeepEquilibrium

Multiscale Deep Equilibrium Models

https://github.com/SinghJasdeep/Projecting-Conflicting-Gradients

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

https://github.com/SirRob1997/Collaborative-Filtering-under-Model-Uncertainty

Predictive Multiplicity in Classification

https://github.com/SirRob1997/collaborative_filtering

Predictive Multiplicity in Classification

https://github.com/SkullFang/ULMFIT_NLP_Classification

Universal Language Model Fine-tuning for Text Classification

https://github.com/SlongLiu/ASL_reproduce

Asymmetric Loss For Multi-Label Classification

https://github.com/Smerity/keras_snli

Neural Semantic Encoders

https://github.com/Socialbird-AILab/BERT-Classification-Tutorial

Universal Language Model Fine-tuning for Text Classification

https://github.com/SohaibAnwaar/lip-Reading-by-Deep-learning

LipNet: End-to-End Sentence-level Lipreading

https://github.com/Soham0/Class2Str

Class2Str: End to End Latent Hierarchy Learning

https://github.com/SonbolYb/Deep-Neuro-Fuzzy

A Deep Neuro-Fuzzy Network for Image Classification

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