#RESIDUAL NEURAL NETWORK SERIES#

Very deep residual networks (those having thousands of layers) have a dimishing return for feature reuse.

Each fraction of improvement in performance costs double the number of layers.

There came a point where deeper residual blocks give diminishing results.ДЄs mentioned in the Wide Residual Network, when a ResNet is thousands of layers deep, there are a few problems we face. This means they perform better in transfer learning in comparison to other networks of the same scale and number of parameters.Д«ut the main focus to improving the accuracy of ResNets was by adding more and more residual blocks to increase the depth of the network. ResNets used in image classification, object detection, and image segmentation.Д«ecause of the residual blocks, ResNets were able to show better generalization capabilities. And hence, we will have a thorough discussion of the paper in this post.ДЇigure 2. Although the paper has a decent number of citations, still, many newcomers in deep learning may not get exposure to this paper. After publication, the paper has gone under a few revisions, and the current one is version 4 published in June 2017. Wide Residual Neural Networks were introduced in the paper Wide Residual Networks by Sergey Zagoruyko andВ Nikos Komodakis. To counter this issue and build even more efficient residual networks, now we have Wide Residual Neural Networks or WRN for short. And this is where we need to take a look at something new. There are diminishing returns after a point. But even just stacking one residual block after the other does not always help.

Because of the residual blocks, residual networks were able to scale to hundreds and even thousands of layers and were still able to get an improvement in terms of accuracy. The residual blocks were very efficient for building deeper neural networks.

When the original ResNet paper was published, it was a big deal in the world of deep learning. We will go through the paper, the implementation details, the network architecture, the experiment results, and the benefits they provide. Also known as WRNs or Wide ResNets for short. 770-778).In this post, we will go in-depth into the understanding of Wide Residual Neural Networks. InProceedings of the IEEE conference on computer vision and pattern recognition 2016 (pp. Deep residual learning for image recognition.

Knowledge about deep learning is an advantage.

Possibility to implement and research a novel approach.

The architectures should be evaluated and verified using different real-world datasets (we have datasets from different domains from sport to medicine that can be used).

#RESIDUAL NEURAL NETWORK SERIES#

The main task is to explore and evaluate the capabilities of ResNet based time series analysis for classification or prediction. The goal is to research and develop new deep neural network architectures that utilize ResNets for the task. ResNets being an advanced CNN is the natural next step to improve time series classification and prediction.

Nevertheless, there exists also work for time series analysis that utilizes convolutional neural networks (CNN). Usually, Recurrent neural networks are used for time series analysis.

Residual neural network