Convolutional neural networks have been a revolution in the field of Computer Vision and are being extensively used for the purpose of image classification, object detection, generation of captions etc. CNNs are mostly considered black boxes where the internal functioning is not known. The objective...
The regulation of workers' working conditions is a crucial issue for achieving social sustainability. This research aims to investigate the reactions to the workers' protests at Amazon, one of the world's largest companies, in the context of the gig economy. The originality of the study lies in...
Building powerful image classification models using very little data Building Autoencoders in Keras A complete guide to using Keras as part of a TensorFlow workflow Introduction to Keras, from University of Waterloo:video-slides Introduction to Deep Learning with Keras, from CERN:video-slides ...
In case 2, the optimized CNN was trained with the dataset from one working load and tested with the other three different working loads, which resulted in a sharp reduction of accuracy. To address this problem, multi-convolutional layer, data augmentation and signal concatenation were proposed ...
Mirror the structure of PyTorch framework. Most core functions defined in functional module. Interfaces (classes wrapping functional) are defined in general snn lib. Expand Down 14 changes: 9 additions & 5 deletions 14 cnn_example.py → examples/cnn_example.py Show comments View file Edit ...
Deep convolutional neural network (DCNN) has obtained great successes for image classification. However, the principle of human visual system (HVS) is not fully investigated and incorporated into the current popular DCNN models. In this work, a novel DCNN model named parallel crossing DCNN (PC–DC...
%cnnConvolve Returns the convolution of the features given by W and b with %the given images % % Parameters: % patchDim - patch (feature) dimension % numFeatures - number of features % images - large images to convolve with, matrix in the form ...
Image classification Multi-task classification Detectnet_v2 FasterRCNN SSD DSSD YOLOv3 YOLOv4 YOLOv4-Tiny RetinaNet MaskRCNN EfficientDet UNet For these networks, the only task that can run multi-node training is train. To invoke multi-node training, simply add the --multi-node argument to the...
Deep learning methods have promoted considerable development in speech recognition, object recognition and image classification [1], [2], [3]. In industrial manufacturing, the failure of machine parts may lead to the decrease in production efficiency and cause irreparable loss. Therefore, fault diagno...
State borders were closed to international and interstate travel for almost two years and there were only short, limited periods of mandated ‘lockdown’ within the state. This meant that WA benefited from largely unrestricted movement within the state. Changes in traffic patterns and public ...