Attention mechanism Implementation for Keras.

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

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

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"

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

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

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

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

Bidirectional LSTM network for speech emotion recognition.

code of Relation Classification via Multi-Level Attention CNNs

Soft attention mechanism for video caption generation

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

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

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

Deep Visual Attention Prediction (TIP18)

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

Machine Reading Comprehension in Tensorflow

Unofficial implementation algorithms of attention models on SNLI dataset