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코드

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

https://github.com/leaderj1001/Billion-scale-semi-supervised-learning

Billion-scale semi-supervised learning for image classification

https://github.com/leaderj1001/Stand-Alone-Self-Attention

Attention Augmented Convolutional Networks

https://github.com/leaplabthu/l2w-den

Multi-Scale Dense Networks for Resource Efficient Image Classification

https://github.com/learn-ensemble/PY-SUMMA

Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions

https://github.com/learn-ensemble/R-SUMMA

Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions

https://github.com/learnables/learn2learn

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

https://github.com/learningtitans/data-depth-design

RECOD Titans at ISIC Challenge 2017

https://github.com/lee-group-cmu/RFCDE

RFCDE: Random Forests for Conditional Density Estimation

https://github.com/leebebeto/pytorch-implementation

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leehangyu/MultiGrain_Application

MultiGrain: a unified image embedding for classes and instances

https://github.com/leejieun51/transformer

A Structured Self-attentive Sentence Embedding

https://github.com/leemsaebom/attention-guided-cam-visual-explanations-of-vision-transformer-guided-by-self-attention

Transformer Interpretability Beyond Attention Visualization

https://github.com/leenaali1114/Hierarchical-Image-Clustering---Unsupervised-Learning

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leishu02/EMNLP2017_DOC

Unseen Class Discovery in Open-world Classification

https://github.com/leob03/E2E_Object_Pose_Estimator

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leokarlin/LaSO

LaSO: Label-Set Operations networks for multi-label few-shot learning

https://github.com/leokarlin/StarNet

StarNet: towards Weakly Supervised Few-Shot Object Detection

https://github.com/leoliao2008/TensorflowMobileOfficialExample

Going Deeper with Convolutions

https://github.com/leon-liangwu/MaskYolo_Caffe

YOLO9000: Better, Faster, Stronger

https://github.com/leon-liangwu/py-caffe-yolo

YOLO9000: Better, Faster, Stronger

https://github.com/leondgarse/Keras_efficientnet_v2

Self-training with Noisy Student improves ImageNet classification

https://github.com/leondgarse/Keras_efficientnet_v2_test

Self-training with Noisy Student improves ImageNet classification

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/levit

LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference

https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mlp_family

ResMLP: Feedforward networks for image classification with data-efficient training

https://github.com/leongatys/DeepTextures

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leongatys/PytorchNeuralStyleTransfer

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/leopard-ai/betty

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

https://github.com/leosampaio/sketchformer

Sketchformer: Transformer-based Representation for Sketched Structure

https://github.com/leot13/BarlowTwins

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/lessw2020/FTSwish

Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning

https://github.com/lessw2020/FTSwishPlus

Flatten-T Swish: a thresholded ReLU-Swish-like activation function for deep learning

https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer

Gradient Centralization: A New Optimization Technique for Deep Neural Networks

https://github.com/lessw2020/auto-adaptive-ai

Partial Convolution based Padding

https://github.com/letthedataconfess/Keras-triplet-loss-mnist

In Defense of the Triplet Loss for Person Re-Identification

https://github.com/leuchine/S-LSTM

Sentence-State LSTM for Text Representation

https://github.com/levubk/AEPA

Semantic Relation Classification via Bidirectional LSTM Networks with Entity-aware Attention using Latent Entity Typing

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