نتایج جستجو برای: convolutional neural network

تعداد نتایج: 836773  

2016
Hua He John Wieting Kevin Gimpel Jinfeng Rao Jimmy J. Lin

We describe an attention-based convolutional neural network for the English semantic textual similarity (STS) task in the SemEval2016 competition (Agirre et al., 2016). We develop an attention-based input interaction layer and incorporate it into our multiperspective convolutional neural network (He et al., 2015), using the PARAGRAM-PHRASE word embeddings (Wieting et al., 2016) trained on parap...

2016
Yuan Liu Yanlin Qian Ke Chen Joni-Kristian Kämäräinen Heikki Huttunen Lixin Fan Jukka Saarinen

Experimenting novel ideas on deep convolutional neural networks (DCNNs) with big datasets is hampered by the fact that network training requires huge computational resources in the terms of CPU and GPU power and hours. One option is to downscale the problem, e.g., less classes and less samples, but this is undesirable with DCNNs whose performance is largely data-dependent. In this work, we take...

2014
Kuniaki Noda Yuki Yamaguchi Kazuhiro Nakadai Hiroshi G. Okuno Tetsuya Ogata

In recent automatic speech recognition studies, deep learning architecture applications for acoustic modeling have eclipsed conventional sound features such as Mel-frequency cepstral coefficients. However, for visual speech recognition (VSR) studies, handcrafted visual feature extraction mechanisms are still widely utilized. In this paper, we propose to apply a convolutional neural network (CNN...

2016
Somshubra Majumdar Ishaan Jain Anand Madhavan Li Wan Matthew Zeiler Sixin Zhang Yann LeCun Jie Huang

Recent developments in the field of deep learning have shown that convolutional networks with several layers can approach human level accuracy in tasks such as handwritten digit classification and object recognition. It is observed that the state-of-the-art performance is obtained from model ensembles, where several models are trained on the same data and their predictions probabilities are ave...

Journal: :CoRR 2016
Wolfgang Fuhl Thiago Santini Gjergji Kasneci Enkelejda Kasneci

Real-time, accurate, and robust pupil detection is an essential prerequisite for pervasive video-based eye-tracking. However, automated pupil detection in real-world scenarios has proven to be an intricate challenge due to fast illumination changes, pupil occlusion, non centered and off-axis eye recording, and physiological eye characteristics. In this paper, we propose and evaluate a method ba...

Journal: :CoRR 2017
Ilke Çugu Eren Sener Emre Akbas

This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images. We show that, for this problem, translation invariance (achieved through max-pooling layers) degrades performance, especially when the network is small, and that the knowledge distillation method can be used to obtain extre...

Journal: :CoRR 2017
Ning Xie Md. Kamruzzaman Sarker Derek Doran Pascal Hitzler Michael Raymer

Many current methods to interpret convolutional neural networks (CNNs) use visualization techniques and words to highlight concepts of the input seemingly relevant to a CNN’s decision. The methods hypothesize that the recognition of these concepts are instrumental in the decision a CNN reaches, but the nature of this relationship has not been well explored. To address this gap, this paper exami...

Journal: :IJMDEM 2016
Zibo Meng Shizhong Han Min Chen Yan Tong

Recognizing facial actions is challenging, especially when they are accompanied with speech. Instead of employing information solely from the visual channel, this work aims to exploit information from both visual and audio channels in recognizing speech-related facial action units (AUs). In this work, two feature-level fusion methods are proposed. The first method is based on a kind of human-cr...

2016
Jianfei Yu Jing Jiang

Relation classification is the task of classifying the semantic relations between entity pairs in text. Observing that existing work has not fully explored using different representations for relation instances, especially in order to better handle the asymmetry of relation types, in this paper, we propose a neural network based method for relation classification that combines the raw sequence ...

2016
Sue Han Lee Yang Loong Chang Chee Seng Chan Paolo Remagnino

In this paper, we describe the architecture of our plant classification system for the LifeClef 2016 challenge [14]. The objective of the task is to identify 1000 species of images of plants corresponding to 7 different plant organs, as well as automatically detecting invasive species from unknown classes. To address the challenge [10], we proposed a plant classification system that uses a conv...

نمودار تعداد نتایج جستجو در هر سال

با کلیک روی نمودار نتایج را به سال انتشار فیلتر کنید