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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

5

코드

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

https://github.com/muellerzr/CodeFest_2019

Regularizing and Optimizing LSTM Language Models

https://github.com/muhaochen/MTransE

Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

https://github.com/muhaochen/MTransE-tf

Multilingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

https://github.com/munir-bd/Korean-POS-Tagger-LSTM

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

https://github.com/myamamoto555/tf-bert

Attention Is All You Need

https://github.com/nachiket273/lookahead_pytorch

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/nachiketaa/bert

Attention Is All You Need

https://github.com/nahliabdelwahed/Face-Image-Deblurring

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/nalbert9/Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/namanphy/style-transfer-through-back-translate

Style Transfer Through Back-Translation

https://github.com/nameoverflow/neuro-cangjie

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/namisan/mt-dnn

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/nasib-ullah/show_attend_and_tell_pytorch

Show, Attend and Tell: Neural Image Caption Generation with Visual Attention

https://github.com/natel9178/transformer-refork

Attention Is All You Need

https://github.com/naver-ai/StyleMapGAN

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/nbansal90/bAbi_QA

Recurrent Neural Network Regularization

https://github.com/ndb796/test2

Disrupting Deepfakes: Adversarial Attacks Against Conditional Image Translation Networks and Facial Manipulation Systems

https://github.com/neburseni/NLP

Attention Is All You Need

https://github.com/necla-ml/Deformable-ConvNets-py3

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/neerav47/Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/nehalelkaref/towards-a-better-isotropy-to-detect-hatespeech

Representation Degeneration Problem in Training Natural Language Generation Models

https://github.com/neil-zeng/asr

Attention-Based Models for Speech Recognition

https://github.com/nekitmm/starnet

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/neqkir/attention-mechanism

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/neubig/nlp-from-scratch-assignment-2022

With Little Power Comes Great Responsibility

https://github.com/neulab/code-bert-score

BERTScore: Evaluating Text Generation with BERT

https://github.com/neulab/knn-transformers

Nearest Neighbor Machine Translation

https://github.com/nfelnlp/nfelnlp

Inseq: An Interpretability Toolkit for Sequence Generation Models

https://github.com/ngocphucck/BERT-questions-classification

Attention Is All You Need

https://github.com/ngovinhtn/JaViCorpus

Combining Advanced Methods in Japanese-Vietnamese Neural Machine Translation

https://github.com/nguyen-nhat-anh/Star-GAN

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

https://github.com/nguyenquangduc2000/AttGAN

Autoencoding beyond pixels using a learned similarity metric

https://github.com/nguyetn89/Anomaly_detection_ICCV2019

Anomaly Detection in Video Sequence with Appearance-Motion Correspondence

https://github.com/nhannguyencsd/vision_cyclegan

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/ni9elf/QANet

QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension

https://github.com/niccolot/ENG-ITA_NMT

Attention Is All You Need

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