55,194 research outputs found
Algorithmic as well as Space and Time comparison of various Deep Learning Algorithms
Deep learning is an artificial intelligence subfield within machine learning. Now- a-days, deep learning has been used in various applications like computer vision, natural language processing, speech recognition, social network filtering, neural machine translation, etc. Deep learning, Convolutional Neural Network (CNN) is a set of deep neural networks mainly designed for image analysis. Deep learning strong ability is mainly due to multiple feature extraction. In this pa- per, we will discuss and compare AlexNet,VGGNet-16,Residual Network(ResNet-50,101,152)
Convolutional neural network based on sparse graph attention mechanism for MRI super-resolution
Magnetic resonance imaging (MRI) is a valuable clinical tool for displaying
anatomical structures and aiding in accurate diagnosis. Medical image
super-resolution (SR) reconstruction using deep learning techniques can enhance
lesion analysis and assist doctors in improving diagnostic efficiency and
accuracy. However, existing deep learning-based SR methods predominantly rely
on convolutional neural networks (CNNs), which inherently limit the expressive
capabilities of these models and therefore make it challenging to discover
potential relationships between different image features. To overcome this
limitation, we propose an A-network that utilizes multiple convolution operator
feature extraction modules (MCO) for extracting image features using multiple
convolution operators. These extracted features are passed through multiple
sets of cross-feature extraction modules (MSC) to highlight key features
through inter-channel feature interactions, enabling subsequent feature
learning. An attention-based sparse graph neural network module is incorporated
to establish relationships between pixel features, learning which adjacent
pixels have the greatest impact on determining the features to be filled. To
evaluate our model's effectiveness, we conducted experiments using different
models on data generated from multiple datasets with different degradation
multiples, and the experimental results show that our method is a significant
improvement over the current state-of-the-art methods.Comment: 12 pages, 6 figure
On End-to-end Multi-channel Time Domain Speech Separation in Reverberant Environments
This paper introduces a new method for multi-channel time domain speech
separation in reverberant environments. A fully-convolutional neural network
structure has been used to directly separate speech from multiple microphone
recordings, with no need of conventional spatial feature extraction. To reduce
the influence of reverberation on spatial feature extraction, a dereverberation
pre-processing method has been applied to further improve the separation
performance. A spatialized version of wsj0-2mix dataset has been simulated to
evaluate the proposed system. Both source separation and speech recognition
performance of the separated signals have been evaluated objectively.
Experiments show that the proposed fully-convolutional network improves the
source separation metric and the word error rate (WER) by more than 13% and 50%
relative, respectively, over a reference system with conventional features.
Applying dereverberation as pre-processing to the proposed system can further
reduce the WER by 29% relative using an acoustic model trained on clean and
reverberated data.Comment: Presented at IEEE ICASSP 202
A Hybrid Convolutional Network and Long Short-Term Memory (HBCNLS) model for Sentiment Analysis on Movie Reviews
This paper proposes a hybrid model (HBCNLS) for sentiment analysis that combines the strengths of multiple machine learning approaches. The model consists of a convolutional neural network (CNN) for feature extraction, a long short-term memory (LSTM) network for capturing sequential dependencies, and a fully connected layer for classification on movie review dataset. We evaluate the performance of the HBCNLS on the IMDb movie review dataset and compare it to other state-of-the-art models, including BERT. Our results show that the hybrid model outperforms the other models in terms of accuracy, precision, and recall, demonstrating the effectiveness of the hybrid approach. The research work also compares the performance of BERT, a pre-trained transformer model, with long short-term memory (LSTM) networks and convolutional neural networks (CNNs) for the task of sentiment analysis on a movie review dataset.
Convolutional neural network model in machine learning methods and computer vision for image recognition: a review
Recently, Convolutional Neural Networks (CNNs) are used in variety of areas including image and pattern recognition, speech recognition, biometric embedded vision, food recognition and video analysis for surveillance, industrial robots and autonomous cars. There are a number of reasons that convolutional neural networks (CNNs) are becoming important. Feature extractors are hand designed during traditional models for image recognition. In CNNs, the weights of the convolutional layer being used for feature extraction in addition to the fully connected layer are applied for classification that are determined during the training process. The objective of this paper is to review a few learning machine methods of convolutional neural network (CNNs) in image recognition. Furthermore, current approaches to image recognition make essential use of machine learning methods. Based on twenty five journal that have been review, this paper focusing on the development trend of convolution neural network (CNNs) model due to various learning method in image recognition since 2000s, which is mainly introduced from the aspects of capturing, verification and clustering. Consequently, deep convolutional neural network (DCNNs) have shown much successful in various machine learning and computer
vision problem because it significant quality gain at a modest increase of computational requirement. This training method also allows models that are composed of multiple processing layers to learn representation of data with multiple levels of abstraction
Deep Learning Approaches for Big Data Analysis
Good representations of data eliminate irrelevant variability of the input data, while preserving the information that is useful for the ultimate task. Among the various ways for learning representation is using deep learning methods. Deep feature hierarchies are formed by stacking unsupervised modules on top of each other, forming multiple non-linear transformations to produce better representations. In this talk, we will first show how deep learning is used for bioactivity prediction of chemical compounds. Molecules are represented as several convolutional neural networks to predict their bioactivity. In addition, a new concept of merging multiple convolutional neural networks and an automatic learning features representation for the chemical compounds was proposed using the values within neurons of the last layer of the CNN architecture. We will also show how the concepts of deep learning is adapted into a deep belief network (DBN) to enhance the molecular similarity searching. The DBN achieves feature abstraction by reconstruction weight for each feature and minimizing the reconstruction error over the whole feature set. The DBN is later enhanced using data fusion to obtain a lower detection error probability and a higher reliability by using data from multiple distributed descriptors. Secondly, we will show how we used deep learning for stock market prediction. Here, we developed a Deep Long Short Term Memory Network model that is able to forecast the crude palm oil price movement with combined factors such as other commodities prices, weather and news sentiments and price movement of crude palm oil. We will also show how we combined stock markets price and financial news and deployed the Long Short Term Memory (LSTM), Recurrent Neural Network (RNN), and Word 2 Vector (Word2Vec) to project the stock prices for the following seven days. Finally, we will show how we exploited deep learning method for the opinion mining and later used it to extract the product's aspects from the user textual review for recommendation systems. Specifically, we employ a multichannel convolutional neural network (MCNN) for two different input layers, namely, word embedding layer and Part-of-speech (POS) tag embedding layer. We will show effectiveness of the proposed model in terms of both aspect extraction and rating prediction performance
Deep Convolutional Neural Networks as Generic Feature Extractors
Recognizing objects in natural images is an intricate problem involving
multiple conflicting objectives. Deep convolutional neural networks, trained on
large datasets, achieve convincing results and are currently the
state-of-the-art approach for this task. However, the long time needed to train
such deep networks is a major drawback. We tackled this problem by reusing a
previously trained network. For this purpose, we first trained a deep
convolutional network on the ILSVRC2012 dataset. We then maintained the learned
convolution kernels and only retrained the classification part on different
datasets. Using this approach, we achieved an accuracy of 67.68 % on CIFAR-100,
compared to the previous state-of-the-art result of 65.43 %. Furthermore, our
findings indicate that convolutional networks are able to learn generic feature
extractors that can be used for different tasks.Comment: 4 pages, accepted version for publication in Proceedings of the IEEE
International Joint Conference on Neural Networks (IJCNN), July 2015,
Killarney, Irelan
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