Using Feature Weights to Improve Performance of Neural Networks

Different features have different relevance to a particular learning problem. Some features are less relevant; while some very important. Instead of selecting the most relevant features using feature selection, an algorithm can be given this knowledge of feature importance based on expert opinion or prior learning. Learning can be faster and more accurate if learners take feature importance into account. Correlation aided Neural Networks (CANN) is presented which is such an algorithm. CANN treats feature importance as the correlation coefficient between the target attribute and the features. CANN modifies normal feed-forward Neural Network to fit both correlation values and training data. Empirical evaluation shows that CANN is faster and more accurate than applying the two step approach of feature selection and then using normal learning algorithms

Deep Adaptive Temporal Pooling for Activity Recognition

Deep neural networks have recently achieved competitive accuracy for human activity recognition. However, there is room for improvement, especially in modeling of long-term temporal importance and determining the activity relevance of different temporal segments in a video. To address this problem, we propose a learnable and differentiable module: Deep Adaptive Temporal Pooling (DATP). DATP applies a self-attention mechanism to adaptively pool the classification scores of different video segments. Specifically, using frame-level features, DATP regresses importance of different temporal segments, and generates weights for them. Remarkably, DATP is trained using only the video-level label. There is no need of additional supervision except video-level activity class label. We conduct extensive experiments to investigate various input features and different weight models. Experimental results show that DATP can learn to assign large weights to key video segments. More importantly, DATP can improve training of frame-level feature extractor. This is because relevant temporal segments are assigned large weights during back-propagation. Overall, we achieve state-of-the-art performance on UCF101, HMDB51 and Kinetics datasets

An Efficient Speech Separation Network Based on Recurrent Fusion Dilated Convolution and Channel Attention

We present an efficient speech separation neural network, ARFDCN, which combines dilated convolutions, multi-scale fusion (MSF), and channel attention to overcome the limited receptive field of convolution-based networks and the high computational cost of transformer-based networks. The suggested network architecture is encoder-decoder based. By using dilated convolutions with gradually increasing dilation value to learn local and global features and fusing them at adjacent stages, the model can learn rich feature content. Meanwhile, by adding channel attention modules to the network, the model can extract channel weights, learn more important features, and thus improve its expressive power and robustness. Experimental results indicate that the model achieves a decent balance between performance and computational efficiency, making it a promising alternative to current mainstream models for practical applications.Comment: Accepted by Interspeech 202

Dynamic learning with neural networks and support vector machines

Neural network approach has proven to be a universal approximator for nonlinear continuous functions with an arbitrary accuracy. It has been found to be very successful for various learning and prediction tasks. However, supervised learning using neural networks has some limitations because of the black box nature of their solutions, experimental network parameter selection, danger of overfitting, and convergence to local minima instead of global minima. In certain applications, the fixed neural network structures do not address the effect on the performance of prediction as the number of available data increases. Three new approaches are proposed with respect to these limitations of supervised learning using neural networks in order to improve the prediction accuracy.;Dynamic learning model using evolutionary connectionist approach . In certain applications, the number of available data increases over time. The optimization process determines the number of the input neurons and the number of neurons in the hidden layer. The corresponding globally optimized neural network structure will be iteratively and dynamically reconfigured and updated as new data arrives to improve the prediction accuracy. Improving generalization capability using recurrent neural network and Bayesian regularization. Recurrent neural network has the inherent capability of developing an internal memory, which may naturally extend beyond the externally provided lag spaces. Moreover, by adding a penalty term of sum of connection weights, Bayesian regularization approach is applied to the network training scheme to improve the generalization performance and lower the susceptibility of overfitting. Adaptive prediction model using support vector machines . The learning process of support vector machines is focused on minimizing an upper bound of the generalization error that includes the sum of the empirical training error and a regularized confidence interval, which eventually results in better generalization performance. Further, this learning process is iteratively and dynamically updated after every occurrence of new data in order to capture the most current feature hidden inside the data sequence.;All the proposed approaches have been successfully applied and validated on applications related to software reliability prediction and electric power load forecasting. Quantitative results show that the proposed approaches achieve better prediction accuracy compared to existing approaches

Accuracy Booster: Performance Boosting using Feature Map Re-calibration

Convolution Neural Networks (CNN) have been extremely successful in solving intensive computer vision tasks. The convolutional filters used in CNNs have played a major role in this success, by extracting useful features from the inputs. Recently researchers have tried to boost the performance of CNNs by re-calibrating the feature maps produced by these filters, e.g., Squeeze-and-Excitation Networks (SENets). These approaches have achieved better performance by Exciting up the important channels or feature maps while diminishing the rest. However, in the process, architectural complexity has increased. We propose an architectural block that introduces much lower complexity than the existing methods of CNN performance boosting while performing significantly better than them. We carry out experiments on the CIFAR, ImageNet and MS-COCO datasets, and show that the proposed block can challenge the state-of-the-art results. Our method boosts the ResNet-50 architecture to perform comparably to the ResNet-152 architecture, which is a three times deeper network, on classification. We also show experimentally that our method is not limited to classification but also generalizes well to other tasks such as object detection.Comment: IEEE Winter Conference on Applications of Computer Vision (WACV), 202