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Large-scale JPEG steganalysis using hybrid deep-learning framework

, , ,  - 2016

Deep learning frameworks have recently achieved superior performance in many pattern recognition problems. However, adoption of deep learning in image steganalysis is still in its initial stage. In this paper we propose a hybrid deep-learning framework for JPEG steganalysis incorporating the domain knowledge behind rich steganalytic models. We pr...


Distributed Deep Q-Learning

, ,  - 2015

We propose a distributed deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning. The model is based on the deep Q-network, a convolutional neural network trained with a variant of Q-learning. Its input is raw pixels and its output is a value function estimating future r...


Feature Learning based Deep Supervised Hashing with Pairwise Labels

, ,  - 2015

Recent years have witnessed wide application of hashing for large-scale image retrieval. However, most existing hashing methods are based on hand-crafted features which might not be optimally compatible with the hashing procedure. Recently, deep hashing methods have been proposed to perform simultaneous feature learning and hash-code learning wit...


Towards deep learning with segregated dendrites

, ,  - 2016

Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the brain optimizes...


TensorLayer: A Versatile Library for Efficient Deep Learning Development

, , , , , ,  - 2017

Deep learning has enabled major advances in the fields of computer vision, natural language processing, and multimedia among many others. Developing a deep learning system is arduous and complex, as it involves constructing neural network architectures, managing training/trained models, tuning optimization process, preprocessing and organizing da...


Deep Learning: A Critical Appraisal

 - 2018

Although deep learning has historical roots going back decades, neither the term "deep learning" nor the approach was popular just over five years ago, when the field was reignited by papers such as Krizhevsky, Sutskever and Hinton's now classic (2012) deep network model of Imagenet. What has the field discovered in the five subsequent years? Aga...


Continuous Deep Q-Learning with Model-based Acceleration

, , ,  - 2016

Model-free reinforcement learning has been successfully applied to a range of challenging problems, and has recently been extended to handle large neural network policies and value functions. However, the sample complexity of model-free algorithms, particularly when using high-dimensional function approximators, tends to limit their applicability...


A C++ library for Multimodal Deep Learning

 - 2015

MDL, Multimodal Deep Learning Library, is a deep learning framework that supports multiple models, and this document explains its philosophy and functionality. MDL runs on Linux, Mac, and Unix platforms. It depends on OpenCV. ...


An exact mapping between the Variational Renormalization Group and Deep Learning

,  - 2014

Deep learning is a broad set of techniques that uses multiple layers of representation to automatically learn relevant features directly from structured data. Recently, such techniques have yielded record-breaking results on a diverse set of difficult machine learning tasks in computer vision, speech recognition, and natural language processing. ...


A Brief Survey of Deep Reinforcement Learning

, , ,  - 2017

Deep reinforcement learning is poised to revolutionise the field of AI and represents a step towards building autonomous systems with a higher level understanding of the visual world. Currently, deep learning is enabling reinforcement learning to scale to problems that were previously intractable, such as learning to play video games directly fro...