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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/facebookresearch/adaptive-span

Augmenting Self-attention with Persistent Memory

https://github.com/facebookresearch/bart_ls

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

https://github.com/facebookresearch/detectron

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

https://github.com/facebookresearch/fairscale

GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding

https://github.com/facebookresearch/fastText/blob/master/docs/aligned-vectors.md

Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

https://github.com/facebookresearch/flowmm

Crystal Diffusion Variational Autoencoder for Periodic Material Generation

https://github.com/facebookresearch/semanticimagetranslation

FlexIT: Towards Flexible Semantic Image Translation

https://github.com/facebookresearch/stochastic_gradient_push

Stochastic Gradient Push for Distributed Deep Learning

https://github.com/facebookresearch/xformers

Attention Is All You Need

https://github.com/faizanahemad/facebook-hateful-memes

The Relative Performance of Ensemble Methods with Deep Convolutional Neural Networks for Image Classification

https://github.com/faizansuhail89/bert

Attention Is All You Need

https://github.com/fajri91/indommlu

Few-shot Learning with Multilingual Language Models

https://github.com/fanchenyou/deepdiary

Show and Tell: A Neural Image Caption Generator

https://github.com/fanchenyou/transformer-study

Attention Is All You Need

https://github.com/fancyerii/bert

Attention Is All You Need

https://github.com/fantasystarwd/Openpose-Depth-CaffeTraining

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

https://github.com/faramarzmunshi/d2l-nlp

Using the Output Embedding to Improve Language Models

https://github.com/farizrahman4u/seq2seq

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

https://github.com/farrell236/SVRnet

Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry

https://github.com/fastai/fastai/blob/master/fastai/text/models/awdlstm.py

Regularizing and Optimizing LSTM Language Models

https://github.com/fbcotter/scatnet_learn

Invariant Scattering Convolution Networks

https://github.com/fciannel/bert_fciannel

Attention Is All You Need

https://github.com/fd873630/RNN-Transducer

Sequence Transduction with Recurrent Neural Networks

https://github.com/fdasilva59/Pix2Pix-Kubeflow-Demo

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/feiyuhuahuo/Yolact_minimal

YOLACT: Real-time Instance Segmentation

https://github.com/feizc/Image-Caption-Baseline

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

https://github.com/feizc/Image-Caption-Pytorch

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

https://github.com/feizc/Machine-Translation-Pytorch

Attention Is All You Need

https://github.com/felixSchober/ABSA-Transformer

Attention Is All You Need

https://github.com/felixriese/CNN-SoilTextureClassification

An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution

https://github.com/feyzaakyurek/XLM-LwLL

Phrase-Based & Neural Unsupervised Machine Translation

https://github.com/fingerrec/dsm-decoupling-scene-motion

Spatial Transformer Networks

https://github.com/fjiangai/mmapis

Attention Is All You Need

https://github.com/fliegenpilz357/amr-metric-suite

AMR Similarity Metrics from Principles

https://github.com/flipz357/amr-metric-suite

AMR Similarity Metrics from Principles

https://github.com/float256/rectified-adam-keras

On the Variance of the Adaptive Learning Rate and Beyond

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