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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/shyam-skk/Parallel_CNN_Transfromer

Attention Is All You Need

https://github.com/shyamsn97/hyper-nn

HyperNetworks

https://github.com/sibozhang/Text2Video

Text-based Editing of Talking-head Video

https://github.com/sidney1505/arc_maml_transformer

Universal Transformers

https://github.com/sidneykingsley/fyp

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/sidneykingsley/pix2pix-tensorflow

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/sigmaister/disrupting-deepfakes-_-

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

https://github.com/sign-language-processing/signwriting-evaluation

SignBank+: Preparing a Multilingual Sign Language Dataset for Machine Translation Using Large Language Models

https://github.com/sign-language-processing/spoken-to-signed-translation

An Open-Source Gloss-Based Baseline for Spoken to Signed Language Translation

https://github.com/sigtot/unet-auto

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/sigtyp/ST2024

The Natural Language Decathlon: Multitask Learning as Question Answering

https://github.com/silgena/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/silvandeleemput/memcnn

Reversible GANs for Memory-efficient Image-to-Image Translation

https://github.com/simnyatsanga/image-caption-generator

Show and Tell: A Neural Image Caption Generator

https://github.com/simon-benigeri/lstm-language-model

Recurrent Neural Network Regularization

https://github.com/simonjisu/NMT

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/simonjisu/annotated-transformer-kr

Attention Is All You Need

https://github.com/sindhura97/STraTS

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

https://github.com/singh-shakti94/Deep-Learning-Project

Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance

https://github.com/sitharakannan/inf

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

https://github.com/siyuofzhou/CNNSeqToSeq

Neural Machine Translation of Rare Words with Subword Units

https://github.com/sjyttkl/Transformer_learning

Attention Is All You Need

https://github.com/skabbit/pix2pix-webcam

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/skoltech-nlp/russe_detox_2022

Language-agnostic BERT Sentence Embedding

https://github.com/skritik098/new_yolact

YOLACT: Real-time Instance Segmentation

https://github.com/skritik098/new_yolact_2

YOLACT: Real-time Instance Segmentation

https://github.com/skt-ai/kobart

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

https://github.com/skytliang/multi-agents-debate

Exploring Human-Like Translation Strategy with Large Language Models

https://github.com/sleepyhead01/vulnurable-code-entities-r-analysis

CodeAttack: Code-Based Adversarial Attacks for Pre-trained Programming Language Models

https://github.com/slme1109/Lyrics_Generator_Using_LSTM

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/slme1109/lyrics-generator

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/smisthzhu/attentionocr

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/snagiri/ECE285_Jarvis_ProjectA

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

https://github.com/snjstudent/DRIT_pp

DRIT++: Diverse Image-to-Image Translation via Disentangled Representations

https://github.com/snoop2head/Deep-Encoder-Shallow-Decoder

Deep Encoder, Shallow Decoder: Reevaluating Non-autoregressive Machine Translation

https://github.com/socc-io/piqaboo

Attention Is All You Need

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