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Biologically-Motivated Deep Learning Method using Hierarchical Competitive Learning

 - 2020

This study proposes a novel biologically-motivated learning method for deep convolutional neural networks (CNNs). The combination of CNNs and back propagation (BP) learning is the most powerful method in recent machine learning regimes. However, it requires large labeled data for training, and this requirement can occasionally become a barrier fo...


Blue River Controls: A toolkit for Reinforcement Learning Control Systems on Hardware

, ,  - 2020

We provide a simple hardware wrapper around the Quanser's hardware-in-the-loop software development kit (HIL SDK) to allow for easy development of new Quanser hardware. To connect to the hardware we use a module written in Cython. The internal QuanserWrapper class handles most of the difficult aspects of interacting with hardware, including the t...


Deep Video Super-Resolution using HR Optical Flow Estimation

, , , , ,  - 2020

Video super-resolution (SR) aims at generating a sequence of high-resolution (HR) frames with plausible and temporally consistent details from their low-resolution (LR) counterparts. The key challenge for video SR lies in the effective exploitation of temporal dependency between consecutive frames. Existing deep learning based methods commonly es...


Fine-Grained Fashion Similarity Learning by Attribute-Specific Embedding Network

, , , , , ,  - 2020

This paper strives to learn fine-grained fashion similarity. In this similarity paradigm, one should pay more attention to the similarity in terms of a specific design/attribute among fashion items, which has potential values in many fashion related applications such as fashion copyright protection. To this end, we propose an Attribute-Specific E...


Regression and Learning with Pixel-wise Attention for Retinal Fundus Glaucoma Segmentation and Detection

,  - 2020

Observing retinal fundus images by an ophthalmologist is a major diagnosis approach for glaucoma. However, it is still difficult to distinguish the features of the lesion solely through manual observations, especially, in glaucoma early phase. In this paper, we present two deep learning-based automated algorithms for glaucoma detection and optic ...


Bayesian task embedding for few-shot Bayesian optimization

, , , ,  - 2020

We describe a method for Bayesian optimization by which one may incorporate data from multiple systems whose quantitative interrelationships are unknown a priori. All general (nonreal-valued) features of the systems are associated with continuous latent variables that enter as inputs into a single metamodel that simultaneously learns the response...


Unpaired Multi-modal Segmentation via Knowledge Distillation

, , ,  - 2020

Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired cross-modality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our...


Tensor-to-Vector Regression for Multi-channel Speech Enhancement based on Tensor-Train Network

, , , , ,  - 2020

We propose a tensor-to-vector regression approach to multi-channel speech enhancement in order to address the issue of input size explosion and hidden-layer size expansion. The key idea is to cast the conventional deep neural network (DNN) based vector-to-vector regression formulation under a tensor-train network (TTN) framework. TTN is a recentl...


Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference

, , , , ,  - 2020

While increasingly deep networks are still in general desired for achieving state-of-the-art performance, for many specific inputs a simpler network might already suffice. Existing works exploited this observation by learning to skip convolutional layers in an input-dependent manner. However, we argue their binary decision scheme, i.e., either fu...


The INTERSPEECH 2020 Deep Noise Suppression Challenge: Datasets, Subjective Speech Quality and Testing Framework

, , , , , , , , , , , ,  - 2020

The INTERSPEECH 2020 Deep Noise Suppression Challenge is intended to promote collaborative research in real-time single-channel Speech Enhancement aimed to maximize the subjective (perceptual) quality of the enhanced speech. A typical approach to evaluate the noise suppression methods is to use objective metrics on the test set obtained by splitt...