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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/achiyaj/vqa-sandbox

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/acmiyaguchi/birdclef-2022

Tile2Vec: Unsupervised representation learning for spatially distributed data

https://github.com/adalld/ecmlpkdd2020

Graph-based semi-supervised learning for relational networks

https://github.com/adalld/wi2020

Graph-based semi-supervised learning for relational networks

https://github.com/adambielski/siamese-triplet

In Defense of the Triplet Loss for Person Re-Identification

https://github.com/adamlin120/Zero-shot_Classification_of_News_Title

Train Once, Test Anywhere: Zero-Shot Learning for Text Classification

https://github.com/adammenges/DARTS-PyTorch

DARTS: Differentiable Architecture Search

https://github.com/adamx97/Data-Science-Advanced-Capstone

Convolutional Neural Networks for Sentence Classification

https://github.com/adamzenith/mapie

Distribution-Free, Risk-Controlling Prediction Sets

https://github.com/adamzhang129/fv_sensor

FingerVision Tactile Sensor Design and Slip Detection Using Convolutional LSTM Network

https://github.com/adapt-python/adapt

Adversarial Discriminative Domain Adaptation

https://github.com/addisonklinke/pytorch-architectures/blob/master/torcharch/conv.py

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

https://github.com/adelnabli/acid

Stochastic Gradient Push for Distributed Deep Learning

https://github.com/adham-elarabawy/nmep_proj1

Unsupervised Representation Learning by Predicting Image Rotations

https://github.com/adham-elarabawy/nn-rotation-predictor

Unsupervised Representation Learning by Predicting Image Rotations

https://github.com/adi2103/AML-CoVe

Convolutional Neural Networks for Sentence Classification

https://github.com/adines/semicompact

Skin Lesion Analysis Toward Melanoma Detection: A Challenge at the 2017 International Symposium on Biomedical Imaging (ISBI), Hosted by the International Skin Imaging Collaboration (ISIC)

https://github.com/adipro7/Handwritten-Captcha-Recognition

EMNIST: an extension of MNIST to handwritten letters

https://github.com/aditya30394/Person-Re-Identification

Random Erasing Data Augmentation

https://github.com/adityaarun1/pytorch_fast-er_rcnn

An Implementation of Faster RCNN with Study for Region Sampling

https://github.com/adlnlp/attention_vl

Iterative Answer Prediction with Pointer-Augmented Multimodal Transformers for TextVQA

https://github.com/adriangrepo/mmbt_lightning

Supervised Multimodal Bitransformers for Classifying Images and Text

https://github.com/adrinta/MAGNET

Multi-Label Text Classification using Attention-based Graph Neural Network

https://github.com/adursun/wsddn.pytorch

Weakly Supervised Deep Detection Networks

https://github.com/adventure2165/Summarization_self-training_with_noisy_student_improves_imagenet_classification

Self-training with Noisy Student improves ImageNet classification

https://github.com/aflah02/Easy-Data-Augmentation-Implementation

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

https://github.com/afrozloya/charrec

Convolutional Neural Networks for Sentence Classification

https://github.com/afzalahmad0203/Numpy-Shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/afzalahmad0203/Tensorflow-Shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/afzalahmad0203/tf_shufflenet

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

https://github.com/agiresearch/InstructGLM

Revisiting Semi-Supervised Learning with Graph Embeddings

https://github.com/agnesdeng/misle

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

https://github.com/agnesdeng/mixgb

XGBoost: A Scalable Tree Boosting System

https://github.com/agongt408/vbranch

In Defense of the Triplet Loss for Person Re-Identification

https://github.com/agrija9/deep-unsupervised-domain-adaptation

Conditional Adversarial Domain Adaptation

https://github.com/agutuyen-dev/darknet

YOLO9000: Better, Faster, Stronger

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