Support Vector Machine Histogram: New Analysis and Architecture Design Method of Deep Convolutional Neural Network

Deep convolutional neural network (DCNN) is a kind of hierarchical neural network models and attracts attention in recent years since it has shown high classification performance. DCNN can acquire the feature representation which is a parameter indicating the feature of the input by learning. However, its internal analysis and the design of the network architecture have many unclear points and it cannot be said that it has been sufficiently elucidated. We propose the novel DCNN analysis method “Support vector machine (SVM) histogram” as a prescription to deal with these problems. This is a method that examines the spatial distribution of DCNN extracted feature representation by using the decision boundary of linear SVM. We show that we can interpret DCNN hierarchical processing using this method. In addition, by using the result of SVM histogram, DCNN architecture design becomes possible. In this study, we designed the architecture of the application to large scale natural image dataset. In the result, we succeeded in showing higher accuracy than the original DCNN

Deep Recurrent Learning for Efficient Image Recognition Using Small Data

Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data for successful learning. Additionally, the feedforward-based hierarchical models do not exploit another important biological learning paradigm, known as recurrency, which ubiquitously exists in the biological visual system and has been shown to be quite crucial for recognition. Consequently, this work aims to develop novel biologically relevant deep recurrent learning models for robust recognition using limited training data. First, we design an efficient deep simultaneous recurrent network (DSRN) architecture for solving several challenging image recognition tasks. The use of simultaneous recurrency in the proposed model improves the recognition performance and offers reduced computational complexity compared to the existing hierarchical deep learning models. Moreover, the DSRN architecture inherently learns meaningful representations of data during the training process which is essential to achieve superior recognition performance. However, probabilistic models such as deep generative models are particularly adept at learning representations directly from unlabeled input data. Accordingly, we show the generalization of the proposed deep simultaneous recurrency concept by developing a probabilistic deep simultaneous recurrent belief network (DSRBN) architecture which is more efficient in learning the underlying representation of the data compared to the state-of-the-art generative models. Finally, we propose a deep recurrent learning framework for solving the image recognition task using small data. We incorporate Bayesian statistics to the DSRBN generative model to propose a deep recurrent generative Bayesian model that addresses the challenge of learning from a small amount of data. Our findings suggest that the proposed deep recurrent Bayesian framework demonstrates better image recognition performance compared to the state-of-the-art models in a small data learning scenario. In conclusion, this dissertation proposes novel deep recurrent learning pipelines, which utilize not only limited training data to achieve improved image recognition performance but also require significantly reduced training parameters

Predicting Alzheimer's Disease by Hierarchical Graph Convolution from Positron Emission Tomography Imaging

Imaging-based early diagnosis of Alzheimer Disease (AD) has become an effective approach, especially by using nuclear medicine imaging techniques such as Positron Emission Topography (PET). In various literature it has been found that PET images can be better modeled as signals (e.g. uptake of florbetapir) defined on a network (non-Euclidean) structure which is governed by its underlying graph patterns of pathological progression and metabolic connectivity. In order to effectively apply deep learning framework for PET image analysis to overcome its limitation on Euclidean grid, we develop a solution for 3D PET image representation and analysis under a generalized, graph-based CNN architecture (PETNet), which analyzes PET signals defined on a group-wise inferred graph structure. Computations in PETNet are defined in non-Euclidean, graph (network) domain, as it performs feature extraction by convolution operations on spectral-filtered signals on the graph and pooling operations based on hierarchical graph clustering. Effectiveness of the PETNet is evaluated on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, which shows improved performance over both deep learning and other machine learning-based methods.Comment: Jiaming Guo, Wei Qiu and Xiang Li contribute equally to this wor

Lightweight Image Super-Resolution with Information Multi-distillation Network

In recent years, single image super-resolution (SISR) methods using deep convolution neural network (CNN) have achieved impressive results. Thanks to the powerful representation capabilities of the deep networks, numerous previous ways can learn the complex non-linear mapping between low-resolution (LR) image patches and their high-resolution (HR) versions. However, excessive convolutions will limit the application of super-resolution technology in low computing power devices. Besides, super-resolution of any arbitrary scale factor is a critical issue in practical applications, which has not been well solved in the previous approaches. To address these issues, we propose a lightweight information multi-distillation network (IMDN) by constructing the cascaded information multi-distillation blocks (IMDB), which contains distillation and selective fusion parts. Specifically, the distillation module extracts hierarchical features step-by-step, and fusion module aggregates them according to the importance of candidate features, which is evaluated by the proposed contrast-aware channel attention mechanism. To process real images with any sizes, we develop an adaptive cropping strategy (ACS) to super-resolve block-wise image patches using the same well-trained model. Extensive experiments suggest that the proposed method performs favorably against the state-of-the-art SR algorithms in term of visual quality, memory footprint, and inference time. Code is available at \url{https://github.com/Zheng222/IMDN}.Comment: To be appear in ACM Multimedia 2019, https://github.com/Zheng222/IMD