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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

4

보존된 평가 기록

5

코드

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

https://github.com/mnikitin/LookaheadOptimizer-mx

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/mnikitin/RAdam

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/mnm-rnd/elsa-voice-asr

Attention-Based Models for Speech Recognition

https://github.com/moabarar/nemar

Unsupervised Multi-Modal Image Registration via Geometry Preserving Image-to-Image Translation

https://github.com/mobarakol/AP-MTL

2017 Robotic Instrument Segmentation Challenge

https://github.com/modelscope/FunCodec

LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT

https://github.com/modelscope/modelscope

Learning to Generalize to More: Continuous Semantic Augmentation for Neural Machine Translation

https://github.com/moein-shariatnia/Deep-Learning/tree/main/Image%20Colorization%20Tutorial

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/moein-shariatnia/Pix2Seq

Attention Is All You Need

https://github.com/mohammad-shirkhani/pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/mohammadAbbasniya/Pix2Pix-from-scratch

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/mohit-kaushik/Pix2PixGAN

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/mohit-kaushik/Satellite_To_MapGeneration

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/mohmmadweb/Generative-HW3-Q5

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/monimoyd/NLPEnglishToPythonCodeUsingTransformer

Attention Is All You Need

https://github.com/monimoyd/NLPMachineTranslationUsingTransformer

Attention Is All You Need

https://github.com/montallen/qrnn-rna-localization

Quasi-Recurrent Neural Networks

https://github.com/moodstocks/gtsrb.torch

Spatial Transformer Networks

https://github.com/moon23k/Attention_Anchors

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/moon23k/Scheduled_Sampling

Attention Is All You Need

https://github.com/moon23k/Transformer_Anchors

Attention Is All You Need

https://github.com/moon23k/Transformer_Archs

Attention Is All You Need

https://github.com/mori97/CycleGAN-chainer

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

https://github.com/mouthful/ClofNet

Learning Gradient Fields for Molecular Conformation Generation

https://github.com/mozilla/subword-nmt

BPE-Dropout: Simple and Effective Subword Regularization

https://github.com/mp2893/gram

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

https://github.com/mp31192/3D-SpatialTransformationLayer-pytorch

Spatial Transformer Networks

https://github.com/mpavlovic/insincere-questions-classifier

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/mronta/CycleGAN-in-Keras

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

https://github.com/mrzhu-cool/pix2pix-pytorch

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/msalhab96/SpeeQ

Sequence Transduction with Recurrent Neural Networks

https://github.com/mshane911/NRP

A Self-supervised Approach for Adversarial Robustness

https://github.com/msmrexe/cs-projects/blob/0af76076c0b1166276a5ac5f89b05a8102be8754/uni-masters/generative-models/GM%20Proj%2003%20-%20Pix2Pix.ipynb

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/msracver/Deformable-ConvNets

R-FCN: Object Detection via Region-based Fully Convolutional Networks

https://github.com/mt-upc/iwslt-2022

SHAS: Approaching optimal Segmentation for End-to-End Speech Translation

https://github.com/mtsomethree/constrdecoding

FUDGE: Controlled Text Generation With Future Discriminators

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