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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/haianhle/ULMFiT-Sentiment

Universal Language Model Fine-tuning for Text Classification

https://github.com/haithink/myWindowsDarknet

YOLO9000: Better, Faster, Stronger

https://github.com/halixness/generative_timesformer_pytorch

Is Space-Time Attention All You Need for Video Understanding?

https://github.com/hamed-ahangari/Separation-Index-of-convolutional-layers-in-EfficientNet-B0

Evaluation of Dataflow through layers of Deep Neural Networks in Classification and Regression Problems

https://github.com/hamed-hosseini/ggcnn

Real-Time Grasp Detection Using Convolutional Neural Networks

https://github.com/hamiddimyati/dd2424-deep-learning

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

https://github.com/hamidmousavi0/reliable-relu-toolbox

FT-ClipAct: Resilience Analysis of Deep Neural Networks and Improving their Fault Tolerance using Clipped Activation

https://github.com/hamidreza-dastmalchi/3dd-tta

Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data

https://github.com/hamzaMahdi/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/han-jia/lastshot

Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions

https://github.com/hananlibpost/ml_final_project

CatBoost: unbiased boosting with categorical features

https://github.com/hanfeng108/Language-Detection

Convolutional Neural Networks for Sentence Classification

https://github.com/hangg7/deformable-kernels

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

https://github.com/hangg7/deformable-kernels/

CondConv: Conditionally Parameterized Convolutions for Efficient Inference

https://github.com/hankpark0706/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/hanmenghan/CPM_Nets

Trusted Multi-View Classification

https://github.com/hannight/amulap

Automatically Identifying Words That Can Serve as Labels for Few-Shot Text Classification

https://github.com/hanqi-qi/sine

Learning Variational Word Masks to Improve the Interpretability of Neural Text Classifiers

https://github.com/hansungj/BayesianNeuralNetworkDiscretizedGibbsSampler

Weight Uncertainty in Neural Networks

https://github.com/hantek/SelfAttentiveSentEmbed

A Structured Self-attentive Sentence Embedding

https://github.com/hanwen0529/DSCSI-GAN

Neural Stain-Style Transfer Learning using GAN for Histopathological Images

https://github.com/haodeqi/BenchmarkingIntentDetection

Benchmarking Natural Language Understanding Services for building Conversational Agents

https://github.com/haofanwang/video-swin-transformer-pytorch

Video Swin Transformer

https://github.com/haoqiwang/vim

The iNaturalist Species Classification and Detection Dataset

https://github.com/happen2me/adda_pytorch

Adversarial Discriminative Domain Adaptation

https://github.com/hardianlawi/MTL-Homoscedastic-Uncertainty

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

https://github.com/hardyqr/cnn-for-stock-market-prediction-pytorch

Trading via Image Classification

https://github.com/harritaylor/torchvggish

CNN Architectures for Large-Scale Audio Classification

https://github.com/harshit0511/Deep-Learning

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

https://github.com/harshitsinghai77/hierarchical-universal-semantic-embeddings

HUSE: Hierarchical Universal Semantic Embeddings

https://github.com/harshmaru7/DilatedConv

Multi-Scale Context Aggregation by Dilated Convolutions

https://github.com/harshraj11584/Paper-Implementation-Overview-Gradient-Descent-Optimization-Algorithms

ADADELTA: An Adaptive Learning Rate Method

https://github.com/harshraj11584/Paper-Implementation-Overview-Gradient-Descent-Optimization-Sebastian-Ruder

ADADELTA: An Adaptive Learning Rate Method

https://github.com/harunshimanto/Neural-Style-Transfer-of-Artistic-Style

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/hassony2/kinetics_i3d_pytorch

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

https://github.com/hav4ik/Hydra

Multi-Task Learning as Multi-Objective Optimization

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