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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/airsplay/vokenization

Contextual Word Representations: A Contextual Introduction

https://github.com/aitorzip/PyTorch-CycleGAN

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

https://github.com/aivolcano/BERT_MRC_CLS

RealFormer: Transformer Likes Residual Attention

https://github.com/aizhanti/jarunc

Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation

https://github.com/ajamjoom/Image-Captions

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

https://github.com/ajoshi80/QANet

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

https://github.com/akanametov/Pix2Pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/akanyaani/minGPTF

Attention Is All You Need

https://github.com/akashe/Python-Code-Generation

Attention Is All You Need

https://github.com/akoepke/audio-retrieval-benchmark

Clotho: An Audio Captioning Dataset

https://github.com/alanjia163/first1

Massive Exploration of Neural Machine Translation Architectures

https://github.com/alankarj/robust_nlp

On Adversarial Examples for Character-Level Neural Machine Translation

https://github.com/albarji/neurowriter

Convolutional Sequence to Sequence Learning

https://github.com/albertogaspar/dts

Deep Learning for Time Series Forecasting: The Electric Load Case

https://github.com/albpurpura/PE4IR

A Study of MatchPyramid Models on Ad-hoc Retrieval

https://github.com/aleksac99/SQuAD-SR

Automatic Spanish Translation of the SQuAD Dataset for Multilingual Question Answering

https://github.com/alex-f1tor/Image-Caption

Show and Tell: A Neural Image Caption Generator

https://github.com/alexanderzyl/stargan

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

https://github.com/alexandra-chron/ntua-slp-wassa-iest2018

Regularizing and Optimizing LSTM Language Models

https://github.com/alexandra-chron/wassa-2018

Regularizing and Optimizing LSTM Language Models

https://github.com/alexbrx/stargan

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

https://github.com/alexdominguez09/pedestrian_direction

Virtual to Real Reinforcement Learning for Autonomous Driving

https://github.com/alexquach/cycleGAN

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

https://github.com/algharak/BERTenhance

Attention Is All You Need

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

Attention Is All You Need

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

LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT

https://github.com/alibabasglab/frcrn

Attention Is All You Need

https://github.com/alicank/Translation-Augmented-LibriSpeech-Corpus

Augmenting Librispeech with French Translations: A Multimodal Corpus for Direct Speech Translation Evaluation

https://github.com/alililia/ascend_U-GAT-IT

U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation

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

Attention Is All You Need

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

Attention Is All You Need

https://github.com/alililia/ms_extend/tree/main/gpu_pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/alililia/vision/tree/main/CycleGAN-MS

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

https://github.com/alimohammadiamirhossein/cora

Cora: Correspondence-aware image editing using few step diffusion

https://github.com/alimpiev/embeddings_persistent_homology

Attention Is All You Need

https://github.com/alisterta/Persian-text-to-speech

Convolutional Sequence to Sequence Learning

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