Unsupervised Deep Feature Transfer for Low Resolution Image Classification

In this paper, we propose a simple while effective unsupervised deep feature transfer algorithm for low resolution image classification. No fine-tuning on convenet filters is required in our method. We use pre-trained convenet to extract features for both high- and low-resolution images, and then feed them into a two-layer feature transfer network for knowledge transfer. A SVM classifier is learned directly using these transferred low resolution features. Our network can be embedded into the state-of-the-art deep neural networks as a plug-in feature enhancement module. It preserves data structures in feature space for high resolution images, and transfers the distinguishing features from a well-structured source domain (high resolution features space) to a not well-organized target domain (low resolution features space). Extensive experiments on VOC2007 test set show that the proposed method achieves significant improvements over the baseline of using feature extraction.Comment: 4 pages, accepted to ICCV19 Workshop and Challenge on Real-World Recognition from Low-Quality Images and Video

Classification of Noncoding RNA Families using Deep Convolutional Neural Networks

In the last decade, the discovery of noncoding RNA (ncRNA) has exploded. Classifying thesencRNA is critical to determining their function. This thesis proposes a new method employing deep convolutional neural networks (CNNs) to classify ncRNA sequences. To this end, this thesis first proposes an efficient approach to convert the RNA sequences into images characterizing their base-pairing probability. As a result, classifying RNA sequences is converted to an image classification problem that can be efficiently solved by available CNN-based classification models. This thesis also considers the folding potential of the ncRNAs in addition to their primary sequence. Based on the proposed approach, a benchmark image classification dataset is generated from the RFAM database of ncRNA sequences. In addition, three classical CNN models and three Siamese network models have been implemented and compared to demonstrate the superior performance and efficiency of the proposed approach. Extensive experimental results show the great potential of using deep learning approaches for RNA classification

Optimization for Training Deep Models and Deep Learning Based Point Cloud Analysis and Image Classification

Deep learning (DL) has dramatically improved the state-of-the-art performances in broad applications of computer vision, such as image recognition, object detection, segmentation, and point cloud analysis. However, the reasons for such huge empirical success of DL still keep elusive theoretically. In this dissertation, to understand DL and improve its efficiency, robustness, and interpretability, we theoretically investigate optimization algorithms for training deep models and empirically explore deep learning based point cloud analysis and image classification. 1). Optimization for Training Deep Models: Neural network training is one of the most difficult optimization problems involved in DL. Recently, it has been attracting more and more attention to understand the global optimality in DL. However, we observe that conventional DL solvers have not been developed intentionally to seek for such global optimality. In this dissertation, we propose a novel approximation algorithm, BPGrad, towards optimizing deep models globally via branch and pruning. The proposed BPGrad algorithm is based on the assumption of Lipschitz continuity in DL, and as a result, it can adaptively determine the step size for the current gradient given the history of previous updates, wherein theoretically no smaller steps can achieve the global optimality. Empirically an efficient solver based on BPGrad for DL is proposed as well, and it outperforms conventional DL solvers such as Adagrad, Adadelta, RMSProp, and Adam in the tasks of object recognition, detection, and segmentation. 2). Deep Learning Based Point Cloud Analysis and Image Classification: The network architecture is of central importance for many visual recognition tasks. In this dissertation, we focus on the emerging field of point clouds analysis and image classification. 2.1) Point cloud analysis: We observe that traditional 6D pose estimation approaches are not sufficient to address the problem where neither a CAD model of the object nor the ground-truth 6D poses of its instances are available during training. We propose a novel unsupervised approach to jointly learn the 3D object model and estimate the 6D poses of multiple instances of the same object in a single end-to-end deep neural network framework, with applications to depth-based instance segmentation. The inputs are depth images, and the learned object model is represented by a 3D point cloud. Specifically, our network produces a 3D object model and a list of rigid transformations on this model to generate instances, which when rendered must match the observed point cloud to minimizing the Chamfer distance. To render the set of instance point clouds with occlusions, the network automatically removes the occluded points in a given camera view. Extensive experiments evaluate our technique on several object models and a varying number of instances in 3D point clouds. Compared with popular baselines for instance segmentation, our model not only demonstrates competitive performance, but also learns a 3D object model that is represented as a 3D point cloud. 2.2) Low quality image classification: We propose a simple while effective unsupervised deep feature transfer network to address the degrading problem of the state-of-the-art classification algorithms on low-quality images. No fine-tuning is required in our method. We use a pre-trained deep model to extract features for both high-resolution (HR) and low-resolution (LR) images, and feed them into a multilayer feature transfer network for knowledge transfer. An SVM classifier is learned directly using these transferred low-resolution features. Our network can be embedded into the state-of-the-art network models as a plug-in feature enhancement module. It preserves data structures in feature space for HR images, and transfers the distinguishing features from a well-structured source domain (HR features space) to a not well-organized target domain (LR features space). Extensive experiments show that the proposed transfer network achieves significant improvements over the baseline method