Image To Latex with DenseNet Encoder and Joint Attention

Log-DenseNet: How to Sparsify a DenseNet

Skip connections are increasingly utilized by deep neural networks to improve accuracy and cost-efficiency. In particular, the recent DenseNet is efficient in computation and parameters, and achieves state-of-the-art predictions by directly connecting each feature layer to all previous ones. However, DenseNet’s extreme connectivity pattern may hinder its scalability to high depths, and in appli...

Image denoising and restoration with CNN-LSTM Encoder Decoder with Direct Attention

Image denoising is always a challenging task in the field of computer vision and image processing. In this paper we have proposed an encoder-decoder model with direct attention, which is capable of denoising and reconstruct highly corrupted images. Our model is consisted of an encoder and a decoder, where encoder is a convolutional neural network and decoder is a multilayer Long Short-Term memo...

a comparison of teachers and supervisors, with respect to teacher efficacy and reflection

supervisors play an undeniable role in training teachers, before starting their professional experience by preparing them, at the initial years of their teaching by checking their work within the proper framework, and later on during their teaching by assessing their progress. but surprisingly, exploring their attributes, professional demands, and qualifications has remained a neglected theme i...

Advances in Joint CTC-Attention Based End-to-End Speech Recognition with a Deep CNN Encoder and RNN-LM

We present a state-of-the-art end-to-end Automatic Speech Recognition (ASR) model. We learn to listen and write characters with a joint Connectionist Temporal Classification (CTC) and attention-based encoder-decoder network. The encoder is a deep Convolutional Neural Network (CNN) based on the VGG network. The CTC network sits on top of the encoder and is jointly trained with the attention-base...

Image to Latex via Neural Networks