Attention mechanism Implementation for Keras.

DeepFill v1/v2 with Contextual Attention and Gated Convolution, CVPR 2018, and ICCV 2019 Oral

Use of Attention Gates in a Convolutional Neural Network / Medical Image Classification and Segmentation

Sequence-to-sequence framework with a focus on Neural Machine Translation based on Apache MXNet

Text classification using deep learning models in Pytorch

Neural Machine Translation with Keras

A Structured Self-attentive Sentence Embedding

Attention OCR Based On Tensorflow

The implementation of "End-to-End Multi-Task Learning with Attention" [CVPR 2019].

Code/Model release for NIPS 2017 paper "Attentional Pooling for Action Recognition"

This repository implements the the encoder and decoder model with attention model for OCR

PyTorch implementation of batched bi-RNN encoder and attention-decoder.

AttentionGAN for Unpaired Image-to-Image Translation & Multi-Domain Image-to-Image Translation

Bidirectional LSTM network for speech emotion recognition.

attention model for entailment on SNLI corpus implemented in Tensorflow and Keras

code of Relation Classification via Multi-Level Attention CNNs

A PyTorch reimplementation for paper Generative Image Inpainting with Contextual Attention (https://arxiv.org/abs/1801.07892)

Soft attention mechanism for video caption generation

A Keras-based library for analysis of time series data using deep learning algorithms.

A recurrent attention module consisting of an LSTM cell which can query its own past cell states by the means of windowed multi-head attention. The formulas are derived from the BN-LSTM and the Transformer Network. The LARNN cell with attention can be easily used inside a loop on the cell state, just like any other RNN. (LARNN)

Code & data accompanying the NAACL2019 paper "Bidirectional Attentive Memory Networks for Question Answering over Knowledge Bases"

Image Captioning based on Bottom-Up and Top-Down Attention model

A neural network to generate captions for an image using CNN and RNN with BEAM Search.

Seq2SeqSharp is a tensor based fast & flexible encoder-decoder deep neural network framework written by .NET (C#). It has many highlighted features, such as automatic differentiation, many different types of encoders/decoders(Transformer, LSTM, BiLSTM and so on), multi-GPUs supported and so on.

ECG Classification

Deep Visual Attention Prediction (TIP18)

Pytorch implementation of Unsupervised Attention-guided Image-to-Image Translation.

The implementation of "Gated Attentive-Autoencoder for Content-Aware Recommendation"

Machine Reading Comprehension in Tensorflow

Caver: a toolkit for multilabel text classification.