Papers with Code 아카이브

Translation

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

0

평가표

4

보존된 평가 기록

5

코드

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

https://github.com/researchworking/scalaremlp

A Practical Method for Constructing Equivariant Multilayer Perceptrons for Arbitrary Matrix Groups

https://github.com/retinal-research/cunsb-rfie

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

https://github.com/retinal-research/ote-gan

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

https://github.com/rezkaaufar/pytorch-cvt

Semi-Supervised Sequence Modeling with Cross-View Training

https://github.com/rgarzonj/LSTMs

Recurrent Neural Network Regularization

https://github.com/ricardordb/bert

Attention Is All You Need

https://github.com/richard-peng-xia/Chinese-Noisy-Text

Attention Is All You Need

https://github.com/richard88888/chinese-noisy-text

Attention Is All You Need

https://github.com/richardbaihe/a3t

A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech Synthesis and Editing

https://github.com/richermans/AudioCaption

Clotho: An Audio Captioning Dataset

https://github.com/richinkabra/CoVe-BCN

Convolutional Sequence to Sequence Learning

https://github.com/rickyHong/AnnieGan-repl

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

https://github.com/rickyHong/Google-BERT-repl

Attention Is All You Need

https://github.com/rickyHong/OpenSeq2Seq-repl

Mixed-Precision Training for NLP and Speech Recognition with OpenSeq2Seq

https://github.com/ricsinaruto/NeuralChatbots-DataFiltering

Attention Is All You Need

https://github.com/ricsinaruto/dialog-eval

Attention Is All You Need

https://github.com/rileynwong/pytorch-seq2seq-joke2punchline

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/rinnakk/japanese-stable-diffusion

Pretraining is All You Need for Image-to-Image Translation

https://github.com/riron1206/kaggle_MoA

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

https://github.com/rish-av/gan_spectral_matching

Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize

https://github.com/rishabhd786/VAE-GAN-PYTORCH

Autoencoding beyond pixels using a learned similarity metric

https://github.com/rishavbb/Image_Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/rishavbhurtel/sentiment-classifier

Attention Is All You Need

https://github.com/rishikksh20/ResMLP-pytorch

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

https://github.com/rishikksh20/VocGAN

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/rishikksh20/melgan

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/rishikksh20/rectified-linear-attention

Sparse Attention with Linear Units

https://github.com/riversdie/chatbot

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/rnwzd/FSPBT-Image-Translation

Interactive Video Stylization Using Few-Shot Patch-Based Training

https://github.com/rohan400/PepperQA

Attention Is All You Need

https://github.com/rohdelab/rcdt_ns_classifier_matlab

Radon cumulative distribution transform subspace modeling for image classification

https://github.com/rohitkuk/Cartoonify

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/rojagtap/abstractive_summarizer

Attention Is All You Need

https://github.com/romankoshkin/transllama

TransLLaMa: LLM-based Simultaneous Translation System

https://github.com/rongjiehuang/transpeech

Direct speech-to-speech translation with discrete units

https://github.com/roomylee/rnn-text-classification-tf

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

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