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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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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/anirudharc/transformer

Attention Is All You Need

https://github.com/anishnarkar/experiments_deeplearning_models

Attention Is All You Need

https://github.com/anitakumarijena/Image_Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/anjalipemmaraju/cyclegan

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

https://github.com/ankurbhatia24/MULTIMODAL-EMOTION-RECOGNITION

Attention Is All You Need

https://github.com/annaorosz/my_qanet_implementation

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

https://github.com/anonymouscode115/autoregressive_inference

Sequence-Level Knowledge Distillation

https://github.com/anshkumar/yolact

YOLACT: Real-time Instance Segmentation

https://github.com/antegrbesa/cnn-image-color-enhancer

DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

https://github.com/antegrbesa/cnn-project

DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

https://github.com/antoinecollas/transformer_neural_machine_translation

Attention Is All You Need

https://github.com/anton-bushuiev/ppiformer

Attention Is All You Need

https://github.com/antoninschrab/fl-mmdagg

MMD Aggregated Two-Sample Test

https://github.com/antoninschrab/mmdagg-paper

MMD Aggregated Two-Sample Test

https://github.com/antonisa/lang2vec

Learning Language Representations for Typology Prediction

https://github.com/anuragreddygv323/Important-stuff

Natural Language Processing: State of The Art, Current Trends and Challenges

https://github.com/anuscode/nrms

Attention Is All You Need

https://github.com/aodongli/LearningFromDraft

Enhanced Neural Machine Translation by Learning from Draft

https://github.com/aodongli/Learning_from_draft

Enhanced Neural Machine Translation by Learning from Draft

https://github.com/appcoreopc/berty

Attention Is All You Need

https://github.com/appraisedev/appraise

Error Span Annotation: A Balanced Approach for Human Evaluation of Machine Translation

https://github.com/appraisedev/ocelot

compare-mt: A Tool for Holistic Comparison of Language Generation Systems

https://github.com/arampacha/clip-rsicd

Adafactor: Adaptive Learning Rates with Sublinear Memory Cost

https://github.com/ari-holtzman/genlm

Regularizing and Optimizing LSTM Language Models

https://github.com/arijitthegame/hybrid-sampling

Using the Output Embedding to Improve Language Models

https://github.com/arkel23/PyTorch-Pretrained-ViT

Attention Is All You Need

https://github.com/arkilpatel/compositional-generalization-seq2seq

Generalization without systematicity: On the compositional skills of sequence-to-sequence recurrent networks

https://github.com/arnab39/cycleGAN-PyTorch

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

https://github.com/arnavdodiedo/dog2sketch

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

https://github.com/arnavdodiedo/sketch2face

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

https://github.com/arnoweng/CheXNet

Learning to diagnose from scratch by exploiting dependencies among labels

https://github.com/arobey1/mbrdl

Multimodal Unsupervised Image-to-Image Translation

https://github.com/artetxem/phrase2vec

Unsupervised Statistical Machine Translation

https://github.com/artitw/tensor2tensor

Tensor2Tensor for Neural Machine Translation

https://github.com/artneer/yolact

YOLACT: Real-time Instance Segmentation

https://github.com/arturo-garza/NMTLexiconModel

Improving Lexical Choice in Neural Machine Translation

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