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코드

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

https://github.com/arunadurai/Eye-For-Blind

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

https://github.com/arvieFrydenlund/awd-lstm-lm

Regularizing and Optimizing LSTM Language Models

https://github.com/as-ideas/deepphonemizer

Transformer based Grapheme-to-Phoneme Conversion

https://github.com/asahi417/lm-question-generation

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/asarigun/MixerGANsformer

MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation

https://github.com/asbjrnmunk/mdd-unet

Bidirectional Learning for Domain Adaptation of Semantic Segmentation

https://github.com/asfathermou/human-computer-interaction

Attention Is All You Need

https://github.com/ashishpatel26/tcn-keras-Examples

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/ashura1234/soccer-line-detection

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/asraman9792/Generative-Adversarial-Networks-implementation-of-KERAS

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

https://github.com/astorfi/neural-machine-translation-from-scratch

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

https://github.com/astorfi/sequence-to-sequence-from-scratch

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

https://github.com/asultan123/DL_LLC_Prefetching

Attention Is All You Need

https://github.com/asyml/texar-pytorch

Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation

https://github.com/asyml/texar/tree/master/examples/text_style_transfer

Unsupervised Text Style Transfer using Language Models as Discriminators

https://github.com/asyrovprog/cs230project

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/atb033/multi_agent_path_planning

Split and Rephrase

https://github.com/atharv6/Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/atpaino/deep-text-corrector

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/atreyasha/semantic-isometry-nmt

Scaling Neural Machine Translation

https://github.com/atun-MEI/AI-Course-Final

Show and Tell: A Neural Image Caption Generator

https://github.com/audio-captioning/clotho-dataset

Clotho: An Audio Captioning Dataset

https://github.com/audreyvm/tfm_gender_bias

Gender in Danger? Evaluating Speech Translation Technology on the MuST-SHE Corpus

https://github.com/augustgw/early-exit-transformer

Attention Is All You Need

https://github.com/austinxy/giraffehd

GIRAFFE HD: A High-Resolution 3D-aware Generative Model

https://github.com/autobotasia/vibert

Attention Is All You Need

https://github.com/autosimtrans/SimulTransBaseline

STACL: Simultaneous Translation with Implicit Anticipation and Controllable Latency using Prefix-to-Prefix Framework

https://github.com/avellal14/AI-for-Art

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

https://github.com/avi33/universalmelgan

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/avisekiit/wacv_2019

Unsupervised Adversarial Visual Level Domain Adaptation for Learning Video Object Detectors from Images

https://github.com/avitalsh/reconst_based_MIA

Membership Inference Attacks are Easier on Difficult Problems

https://github.com/avivga/lord-pytorch

Demystifying Inter-Class Disentanglement

https://github.com/avmb/marian-mbart

Multilingual Denoising Pre-training for Neural Machine Translation

https://github.com/avshalomc/subtract

Style Transfer Through Back-Translation

https://github.com/awalther/scibart

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/awesomephant/vf-installation

Image-to-Image Translation with Conditional Adversarial Networks

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