Fast Sparse Coding Inference with Historical Information Recently, timeunfolded recurrent neural network (RNN) based algorithms are successfully applied for fast sparse coding (SC) inference, such as LISTA and SLSTM. However, these methods do not properly exploit the historical information which is proved to speed up the convergence. In this paper, we propose a novel RNNbased SC inference framework with attention mechanism to efficiently incorporate the related historical information. The proposed framework consists of an attention network and a timeunfolded RNN, where the RNN generates the historical information and the attention network automatically determines the importance of these historical values for the current updating. The final sparse code is obtained by passing the context vectors generated from the attention network to a soft shrinkage function. Extensive experiments on convergence analysis and image classification tasks demonstrate that our network achieves impressive improvements on SC inference in terms of the quality of estimated sparse codes and the inference time. Moreover, the proposed network can be easily extended into a supervised version to further improve the classification accuracy.

A Comparative Study of AttentionBased EncoderDecoder Approaches to Natural Scene Text Recognition Attentionbased encoderdecoder approaches have shown promising results in scene text recognition. In the literature, models with different encoders, decoders and attention mechanisms have been proposed and compared on isolated word recognition tasks, where the models are trained on either synthetic word images or a small set of realworld images. In this paper, we investigate different components of the attention based framework and compare its performance with a CNNDBLSTMCTC based approach on largescale realworld scene text sentence recognition tasks. We train character models by using more than 1.6M realworld text lines and compare their performance on test sets collected from a variety of realworld scenarios. Our results show that (1) attention on a twodimensional feature map can yield better performance than onedimensional one and an RNN based decoder performs better than CNN based one; (2) attentionbased approaches can achieve higher recognition accuracy than CNNDBLSTMCTC based approaches on isolated word recognition tasks, but perform worse on sentence recognition tasks; (3) it is more effective and efficient for CNNDBLSTMCTC based approaches to leverage an explicit language model to boost recognition accuracy.