Transfer Learning is the reuse of a pre-trained model on a new problem. (Towards Data Science)
Transfer Learning in AI is a method where a model is developed for a specific task, which is used as the initial steps for another model for other tasks. Deep Convolutional Neural Networks in deep learning take an hour or day to train the mode if the dataset we are playing is vast. The approach is we reuse the weights of the pre-trained model, which was trained for some standard Computer Vision datasets such as Image classification (Image Net). Extensive deep Convolutional networks for large-scale image classification are available in Keras, which we can directly import and can be used with their pre-trained weights. Let's now understand how to use VGG16 pre-trained on 10,000 categories(Image Net) for the Distracted driver Detection dataset.
Tensorflow is one of the highly used libraries for Machine Learning. It has built-in support for Keras. We can easily call functions related to Keras by using the tf.keras module. Computer Vision is one of the most interesting branches of machine learning. The ImageNet dataset was the turning point for researchers related to Computer Vision as it provided a large set of images for Object detection.
The cost of annotating training data has traditionally been a bottleneck for supervised learning approaches. The problem is further exacerbated when supervised learning is applied to a number of correlated tasks simultaneously since the amount of labels required scales with the number of tasks. To mitigate this concern, we propose an active multitask learning algorithm that achieves knowledge transfer between tasks. The approach forms a so-called committee for each task that jointly makes decisions and directly shares data across similar tasks. Our approach reduces the number of queries needed during training while maintaining high accuracy on test data. Empirical results on benchmark datasets show significant improvements on both accuracy and number of query requests.
Image semantic segmentation is one of the most significant areas of research and engineering in the computer vision domain. From segmenting pedestrians and cars for autonomous drive  to segmentation and localization of pathology in medical images , there are several use-cases of image segmentation. With the wide-spread use of deep learning models for end-to-end delivery for machine learning (ML) models, the U-net model has emerged as a scalable solution across autonomous drive and medical imaging use-cases [3–4]. However, most existing papers and methods implement binary classification tasks for detecting objects/regions of interest over the backgrounds . In this hands-on tutorial we will review how to start from a binary semantic segmentation task and transfer the learning to suit multi-class image segmentation tasks.
If you are still having any trouble understanding the process, it may help to think of all our workers as individuals working on the same puzzle problem. At the end of the epoch, they all hand their findings back to the master node, which combines the partial solutions each one has submitted. Then everyone gets a copy of this combined solution, which is still not complete, and they start working on it again for another epoch. The difference is that now they have a head start thanks to everyone's combined work.
It has been established that Speech Affect Recognition for low resource languages is a difficult task. Here we present a Transfer learning based Speech Affect Recognition approach in which: we pre-train a model for high resource language affect recognition task and fine tune the parameters for low resource language using Deep Residual Network. Here we use standard four data sets to demonstrate that transfer learning can solve the problem of data scarcity for Affect Recognition task. We demonstrate that our approach is efficient by achieving 74.7 percent UAR on RAVDESS as source and Urdu data set as a target. Through an ablation study, we have identified that pre-trained model adds most of the features information, improvement in results and solves less data issues. Using this knowledge, we have also experimented on SAVEE and EMO-DB data set by setting Urdu as target language where only 400 utterances of data is available. This approach achieves high Unweighted Average Recall (UAR) when compared with existing algorithms.
Advanced machines never fail to leave men in awe. But only researchers who worked behind the machines know how much time, cost and data it took to become a stage stealer. Training an algorithm that employs various features in a machine is quite nerve-wracking. But tech geeks have found a solution using transfer learning. Besides, companies are also unveiling a mixture of technologies like deep learning neural networks and machine learning to come up with futuristic machines.