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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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5

코드

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

https://github.com/isosnovik/sesn

Scale-Equivariant Steerable Networks

https://github.com/isrugeek/semcolour

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/itsss/ganimorph

Improving Shape Deformation in Unsupervised Image-to-Image Translation

https://github.com/itsuki8914/CycleGAN-TF

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

https://github.com/itsuki8914/pix2pix-automaticColorization

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/itsuki8914/starGAN-LSGAN

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

https://github.com/ivankunyankin/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/iver56/automatic-video-colorization

Automatic Temporally Coherent Video Colorization

https://github.com/iwangjian/ByteCup2018

Regularizing and Optimizing LSTM Language Models

https://github.com/iwyoo/tf_thinplatespline

Spatial Transformer Networks

https://github.com/ixsluo/cond-cdvae

GemNet: Universal Directional Graph Neural Networks for Molecules

https://github.com/jackaduma/CycleGAN-VC2

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/jackjhliu/End-to-End-Mandarin-ASR

Attention-Based Models for Speech Recognition

https://github.com/jackjhliu/Pytorch-End-to-End-ASR-on-TIMIT

Attention-Based Models for Speech Recognition

https://github.com/jacksonkaunismaa/neural-music

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/jackyjsy/SGGAN

Segmentation Guided Image-to-Image Translation with Adversarial Networks

https://github.com/jacobkrantz/NeuralSum

Abstractive Summarization Using Attentive Neural Techniques

https://github.com/jadore801120/attention-is-all-you-need-pytorch

Attention Is All You Need

https://github.com/jageshmaharjan/BERT_Service

Attention Is All You Need

https://github.com/jaingaurav3/GAN-Hacks

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/jaisenbe58r/NLP-Transformer_Translator

Attention Is All You Need

https://github.com/jakeret/tcn

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

https://github.com/jaketae/realformer

RealFormer: Transformer Likes Residual Attention

https://github.com/jaketae/res-mlp

ResMLP: Feedforward networks for image classification with data-efficient training

https://github.com/jamesdvance/predicting_clicks

Attention Is All You Need

https://github.com/jamestjw/OCR

Evaluating Sequence-to-Sequence Models for Handwritten Text Recognition

https://github.com/jangjoongkeon/JK

Attention Is All You Need

https://github.com/jangsoopark/AConvNet-pytorch

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

https://github.com/jarl93/joeynmt-modified

Sockeye: A Toolkit for Neural Machine Translation

https://github.com/jason9693/cycleGAN-tensorflow-v2

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

https://github.com/jasonppy/promptingwhisper

How2: A Large-scale Dataset for Multimodal Language Understanding

https://github.com/jay-z20/yolact-paddle

YOLACT: Real-time Instance Segmentation

https://github.com/jayChung0302/DeepFilter

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

https://github.com/jaykshirsagar05/captionify

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

https://github.com/jaywalnut310/Vector-Quantized-Autoencoders

Theory and Experiments on Vector Quantized Autoencoders

https://github.com/jaywalnut310/melgan-pytorch

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

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