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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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5

코드

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

https://github.com/oliverwatts/ophelia

Confidence through Attention

https://github.com/ololo123321/maestro

Attention Is All You Need

https://github.com/omerb01/puq

Palette: Image-to-Image Diffusion Models

https://github.com/omid-d/Pix2Pix-for-Cityscapes-Dataset

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/oncescuandreea/audio-retrieval

Clotho: An Audio Captioning Dataset

https://github.com/oneTimePad/conv-nmt

Convolutional Sequence to Sequence Learning

https://github.com/onedayatatime0923/Cycle_Mcd_Gan

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

https://github.com/oneway3124/disaggregation-vrnn

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

https://github.com/open-finance-lab/finlora

BERTScore: Evaluating Text Generation with BERT

https://github.com/open-mmlab/mmclassification

Attention Is All You Need

https://github.com/open-mmlab/mmdetection

YOLACT: Real-time Instance Segmentation

https://github.com/open-mmlab/mmgeneration

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/open-mmlab/mmtracking

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

https://github.com/opendot/ml4a-invisible-cities

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/opennmt/ctranslate2

Attention Is All You Need

https://github.com/oplatek/e2end

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

https://github.com/orenkatzir/VN-SPD

Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons

https://github.com/outcastofmusic/quick-nlp

Massive Exploration of Neural Machine Translation Architectures

https://github.com/owainwest/uniprot_bert

Attention Is All You Need

https://github.com/owennju/fgrada

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation

https://github.com/paarthneekhara/byteNet-tensorflow

Neural Machine Translation in Linear Time

https://github.com/pandegroup/reaction_prediction_seq2seq

Retrosynthetic reaction prediction using neural sequence-to-sequence models

https://github.com/paolanu/BERT_epitope

Attention Is All You Need

https://github.com/papar22/returnn-my-branch

RETURNN as a Generic Flexible Neural Toolkit with Application to Translation and Speech Recognition

https://github.com/parcorelab/comscribe

Attention Is All You Need

https://github.com/park-cheol/ASR-Conformer

Lite Transformer with Long-Short Range Attention

https://github.com/patHutchings/TCN

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

https://github.com/patrickhart/jaxdl

Attention Is All You Need

https://github.com/pauldebryas/SND_trackers_My_data

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/paulskeie/stadnamn

Attention Is All You Need

https://github.com/pbcquoc/transformer

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/pcummer/cycle-gan-keras

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

https://github.com/pcyin/pytorch_basic_nmt

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/pengboxiangshang/siamrpn

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

https://github.com/pengming617/bert_classification

Attention Is All You Need

https://github.com/pengshuang/Joint-Slot-Filling

Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling

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