14 research outputs found
Enhanced CNN for image denoising
Owing to flexible architectures of deep convolutional neural networks (CNNs),
CNNs are successfully used for image denoising. However, they suffer from the
following drawbacks: (i) deep network architecture is very difficult to train.
(ii) Deeper networks face the challenge of performance saturation. In this
study, the authors propose a novel method called enhanced convolutional neural
denoising network (ECNDNet). Specifically, they use residual learning and batch
normalisation techniques to address the problem of training difficulties and
accelerate the convergence of the network. In addition, dilated convolutions
are used in the proposed network to enlarge the context information and reduce
the computational cost. Extensive experiments demonstrate that the ECNDNet
outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201
Automatic artifact removal of resting-state fMRI with Deep Neural Networks
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique for
studying brain activity. During an fMRI session, the subject executes a set of
tasks (task-related fMRI study) or no tasks (resting-state fMRI), and a
sequence of 3-D brain images is obtained for further analysis. In the course of
fMRI, some sources of activation are caused by noise and artifacts. The removal
of these sources is essential before the analysis of the brain activations.
Deep Neural Network (DNN) architectures can be used for denoising and artifact
removal. The main advantage of DNN models is the automatic learning of abstract
and meaningful features, given the raw data. This work presents advanced DNN
architectures for noise and artifact classification, using both spatial and
temporal information in resting-state fMRI sessions. The highest performance is
achieved by a voting schema using information from all the domains, with an
average accuracy of over 98% and a very good balance between the metrics of
sensitivity and specificity (98.5% and 97.5% respectively).Comment: Under Review, ICASSP 202
Training Implicit Networks for Image Deblurring using Jacobian-Free Backpropagation
Recent efforts in applying implicit networks to solve inverse problems in
imaging have achieved competitive or even superior results when compared to
feedforward networks. These implicit networks only require constant memory
during backpropagation, regardless of the number of layers. However, they are
not necessarily easy to train. Gradient calculations are computationally
expensive because they require backpropagating through a fixed point. In
particular, this process requires solving a large linear system whose size is
determined by the number of features in the fixed point iteration. This paper
explores a recently proposed method, Jacobian-free Backpropagation (JFB), a
backpropagation scheme that circumvents such calculation, in the context of
image deblurring problems. Our results show that JFB is comparable against
fine-tuned optimization schemes, state-of-the-art (SOTA) feedforward networks,
and existing implicit networks at a reduced computational cost
Design and analysis of recurrent neural network models with nonālinear activation functions for solving timeāvarying quadratic programming problems
A special recurrent neural network (RNN), that is the zeroing neural network (ZNN), is adopted to find solutions to timeāvarying quadratic programming (TVQP) problems with equality and inequality constraints. However, there are some weaknesses in activation functions of traditional ZNN models, including convex restriction and redundant formulation. With the aid of different activation functions, modified ZNN models are obtained to overcome the drawbacks for solving TVQP problems. Theoretical and experimental research indicate that the proposed models are better and more effective at solving such TVQP problems
Numericalādiscreteāschemeāincorporated recurrent neural network for tasks in natural language processing
A variety of neural networks have been presented to deal with issues in deep learning in the last decades. Despite the prominent success achieved by the neural network, it still lacks theoretical guidance to design an efficient neural network model, and verifying the performance of a model needs excessive resources. Previous research studies have demonstrated that many existing models can be regarded as different numerical discretizations of differential equations. This connection sheds light on designing an effective recurrent neural network (RNN) by resorting to numerical analysis. Simple RNN is regarded as a discretisation of the forward Euler scheme. Considering the limited solution accuracy of the forward Euler methods, a Taylor-type discrete scheme is presented with lower truncation error and a Taylor-type RNN (T-RNN) is designed with its guidance. Extensive experiments are conducted to evaluate its performance on statistical language models and emotion analysis tasks. The noticeable gains obtained by T-RNN present its superiority and the feasibility of designing the neural network model using numerical methods