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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/alexgcsa/AutoMLC

Multi-label classification search space in the MEKA software

https://github.com/alexgkendall/SegNet-Tutorial

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

https://github.com/alexgkendall/caffe-segnet

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

https://github.com/alexismailov2/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/alexquach/prototype-based-domain-adaptation

Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions

https://github.com/alexub/jax-meta-learning

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

https://github.com/alge24/eigenpooling

Graph Convolutional Networks with EigenPooling

https://github.com/algoashutosh/sentiment-analysis-project

Universal Language Model Fine-tuning for Text Classification

https://github.com/algorine/nokore

Semantic Classification of Tabular Datasets via Character-Level Convolutional Neural Networks

https://github.com/alia21/VechicleDetection-and-Traccking

YOLO9000: Better, Faster, Stronger

https://github.com/alibaba-damo-academy/spokennlp

BERT for Joint Intent Classification and Slot Filling

https://github.com/alibaba/EasyNLP

CLUE: A Chinese Language Understanding Evaluation Benchmark

https://github.com/alibaba/EasyNLP/tree/master/examples/fewshot_learning

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

https://github.com/aliengirlliv/teachable

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

https://github.com/alikhodadoost/Persian-Sentence-Augmenter

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

https://github.com/alililia/ms_contrib/tree/main/ascend_gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/alililia/ms_contrib/tree/main/ascend_vgg19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/alililia/ms_contrib/tree/main/gpu_gcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/alililia/ms_contrib/tree/main/gpu_vgg19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/alinstein/X_RAY

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

https://github.com/alipsgh/codes-for-moa

McDiarmid Drift Detection Methods for Evolving Data Streams

https://github.com/alisha17/benchmarks

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/allanzelener/YAD2K

YOLO9000: Better, Faster, Stronger

https://github.com/allenai/aspire

SPECTER: Document-level Representation Learning using Citation-informed Transformers

https://github.com/allenhaozhu/ease

Leveraging the Feature Distribution in Transfer-based Few-Shot Learning

https://github.com/alonjacovi/interpreting-cnn-for-text

Understanding Convolutional Neural Networks for Text Classification

https://github.com/alpemek/aerial-segmentation

Learning Aerial Image Segmentation from Online Maps

https://github.com/alphadl/darts.pytorch1.1

DARTS: Differentiable Architecture Search

https://github.com/alrope123/prompt-waywardness

Character-level Convolutional Networks for Text Classification

https://github.com/altaga/Pytorch-Driving-Guardian

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

https://github.com/alvinchangw/COCON_ICLR2021

Poison Attacks against Text Datasets with Conditional Adversarially Regularized Autoencoder

https://github.com/alzmzyy/pointtransformer

Point Transformer

https://github.com/amagooda/SummaRuNNer_coattention

Universal Language Model Fine-tuning for Text Classification

https://github.com/amamov/vgg16-vs-resnet

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/amanjain1397/HUSE

HUSE: Hierarchical Universal Semantic Embeddings

https://github.com/amazon-research/dse

Prototypical Networks for Few-shot Learning

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