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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/Belval/NRTR

Attention Is All You Need

https://github.com/BenjiKCF/AWD-LSTM-sentiment-classifier

Regularizing and Optimizing LSTM Language Models

https://github.com/Beny-Maleki/Pix2PixTorch

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/Bhavnicksm/vanilla-transformer-jax

Attention Is All You Need

https://github.com/BigBinnie/Benchmarking-LLM-based-Machine-Translation-on-Cultural-Awareness

Benchmarking Machine Translation with Cultural Awareness

https://github.com/BigThreeMI/Utils

YOLACT: Real-time Instance Segmentation

https://github.com/Bingwen-Hu/DRIT

Diverse Image-to-Image Translation via Disentangled Representations

https://github.com/BioinfoMachineLearning/DeepRefine

Geometric Transformers for Protein Interface Contact Prediction

https://github.com/Blade6570/Learningimage-to-imagetranslationusingpairedandunpairedtrainingsamples

Learning image-to-image translation using paired and unpaired training samples

https://github.com/BramVanroy/astred

Word Alignment by Fine-tuning Embeddings on Parallel Corpora

https://github.com/BraveDrXuTF/vae-gan-code-for-reinforced-panel

Autoencoding beyond pixels using a learned similarity metric

https://github.com/BrianPulfer/PapersReimplementations

Attention Is All You Need

https://github.com/BrookInternSOMA/pix2pix-barcode

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/BugOMan/summary_generator

Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks

https://github.com/CGCL-codes/naturalcc

Attention Is All You Need

https://github.com/CKPOON0619/Kaggle2Sigma

Convolutional Sequence to Sequence Learning

https://github.com/CKRC24/Listen-and-Translate

Attention-Based Models for Speech Recognition

https://github.com/COMP6248-Reproducability-Challenge/Differentiables_FastAccurateTextClassification

Learning to Skim Text

https://github.com/COMP6248-Reproducability-Challenge/LookaheadOptimizer

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/COMP6248-Reproducability-Challenge/Transfer-Learning

Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation

https://github.com/CS2470FinalProject/X-GAN

XGAN: Unsupervised Image-to-Image Translation for Many-to-Many Mappings

https://github.com/CSDN-AI7/image_caption

Show and Tell: A Neural Image Caption Generator

https://github.com/CSTR-Edinburgh/ophelia

Confidence through Attention

https://github.com/CV-Reimplementation/FUNIT-Reimplementation

Few-Shot Unsupervised Image-to-Image Translation

https://github.com/CVxTz/music_genre_classification

Attention Is All You Need

https://github.com/CaelynCheung1996/QuickNAT_tensorflow_V2

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/Caesarzhang/bert-zh

Attention Is All You Need

https://github.com/CarloRadice/depth-and-motion-learning

Unsupervised Monocular Depth Learning in Dynamic Scenes

https://github.com/CarpdiemLiang/style_transfer

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

https://github.com/Cathy-t/HELLO_image

Show and Tell: A Neural Image Caption Generator

https://github.com/Cenrax/Image-Captioning-with-Translation-

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/ChaiBapchya/mxnet-hardmax_machine_translation

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/ChandhiniG/Generate-Stylized-Image-from-Edges

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch

Attention Is All You Need

https://github.com/Charliesgithub20221030/BERT

Attention Is All You Need

https://github.com/Chatha-Sphere/pno-ai

Self-Attention with Relative Position Representations

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