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VGG 16 and Inception-v3 networks were trained using a texture dataset of muddied and clean cows. A new dataset with 600 images that is similar to the actual texture dataset was introduced and used to train the networks. The method used to train the networks was transfer learning. ImageNet weights were trained using the similar dataset, then the newly trained weights were trained again using the actual...
We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain based on the statistical properties of a labeled source domain. Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain...
This paper deals with the problem of audio source separation. To handle the complex and ill-posed nature of the problems of audio source separation, the current state-of-the-art approaches employ deep neural networks to obtain instrumental spectra from a mixture. In this study, we propose a novel network architecture that extends the recently developed densely connected convolutional network (DenseNet),...
With the advent of new technologies in the field of medicine, there is rising awareness of biomechanisms, and we are better able to treat ailments than we could earlier. Deep learning has helped a lot in this endeavor. This paper deals with the application of deep learning in brain tumor segmentation. Brain tumors are difficult to segment automatically given the high variability in the shapes and...
Crowd counting on still images is very challenging due to heavy occlusions and scale variations. In this paper, we aim to develop a method that can accurately estimate the crowd count from a still image. Recently, convolutional neural networks have been shown effective in many computer vision tasks including crowd counting. To this end, we propose a fully convolutional network (FCN) architecture to...
Demand for highly energy-efficient coprocessor for the inference computation of deep neural networks is increasing. We propose the time-domain neural network (TDNN), which employs time-domain analog and digital mixed-signal processing (TDAMS) that uses delay time as the analog signal. TDNN not only exploits energy-efficient analog computing, but also enables fully spatially unrolled architecture by...
Support Vector Machine (SVM) is a linear binary classifier that requires a kernel function to handle non-linear problems. Most previous SVM implementations for embedded systems in literature were built targeting a certain application; where analyses were done through comparison with software implementations only. The impact of different application datasets towards SVM hardware performance were not...
Non-uniform blind deblurring for general dynamic scenes is a challenging computer vision problem as blurs arise not only from multiple object motions but also from camera shake, scene depth variation. To remove these complicated motion blurs, conventional energy optimization based methods rely on simple assumptions such that blur kernel is partially uniform or locally linear. Moreover, recent machine...
To predict a set of diverse and informative proposals with enriched representations, this paper introduces a differentiable Determinantal Point Process (DPP) layer that is able to augment the object detection architectures. Most modern object detection architectures, such as Faster R-CNN, learn to localize objects by minimizing deviations from the ground truth, but ignore correlation between multiple...
In this paper we utilize the first large-scale "in-the-wild" (Aff-Wild) database, which is annotated in terms of the valence-arousal dimensions, to train and test an end-to-end deep neural architecture for the estimation of continuous emotion dimensions based on visual cues. The proposed architecture is based on jointly training convolutional (CNN) and recurrent neural network (RNN) layers,...
Human action recognition from skeletal data is a hot research topic and important in many open domain applications of computer vision, thanks to recently introduced 3D sensors. In the literature, naive methods simply transfer off-the-shelf techniques from video to the skeletal representation. However, the current state-of-the-art is contended between to different paradigms: kernel-based methods and...
Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and overflow-resilient limiter is presented. For different applications, different kernels can be chosen and configured...
The advantage of prototype based learning vector quantizers are the intuitive and simple model adaptation as well as the easy interpretability of the prototypes as class representatives for the class distribution to be learned. Although they frequently yield competitive performance and show robust behavior nowadays powerful alternatives have increasing attraction. Particularly, deep architectures...
Machine Learning techniques such as Support Vector Machines (SVM) have found applications in many fields, e.g. in Wireless Sensor Networks (WSN) and sensor data processing in general. Especially in the case of WSN energy is very limited as agents solely operate based on battery power after they have been deployed, therefore energy efficiency is of great importance. Furthermore, agents are supposed...
To explore the potential of training complex deep neural networks (DNNs) on other commercial chips rather than GPUs, we report our work on swDNN, which is a highly-efficient library for accelerating deep learning applications on the newly announced world-leading supercomputer, Sunway TaihuLight. Targeting SW26010 processor, we derive a performance model that guides us in the process of identifying...
Stellar Classification is based on their spectral characteristics. In order to improve performance rates previously reported, like those based on statistical analysis or data transformations, classifiers based on computational intelligence provide a high level of accuracy no matter the presented high level of non-linearity or high dimensionality characteristics of data. In this paper, the star's classification...
Reliable traffic light detection and classification is crucial for automated driving in urban environments. Currently, there are no systems that can reliably perceive traffic lights in real-time, without map-based information, and in sufficient distances needed for smooth urban driving. We propose a complete system consisting of a traffic light detector, tracker, and classifier based on deep learning,...
Nowadays, developing effective techniques able to deal with data coming from structured domains is becoming crucial. In this context kernel methods are the state-of-the-art tool widely adopted in real-world applications that involve learning on structured data. Contrarily, when one has to deal with unstructured domains, deep learning methods represent a competitive, or even better, choice. In this...
This paper presents a simulated memristor crossbar based Convolutional Neural Network (CNN). Deep networks implemented on GPU clusters have become the state of the art in providing excellent classification ability, at the cost of a more complex data manipulation process. In this work we show that once deep networks are trained, the analog crossbar circuits in this paper can parallelize the recognition...
Increasing architectural diversity makes performance portability extremely important for parallel simulation codes. Emerging on-node parallelization frameworks such as Kokkos and RAJA decouple the work done in kernels from the parallelization mechanism, allowing for a single source kernel to be tuned for different architectures at compile time. However, computational demands in production applications...
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