Humans have a very unique ability to learn while they carry out their day-to-day tasks. They have a tendency of forming a logic from their gained knowledge which they use while performing a different set of tasks. Well did you know computers could do that too? Come along I'll show you how this trick works which goes by the name of Transfer learning. To understand better let's ask ourselves some questions: Training models may sometime take weeks even on multiple GPUs so why not just save ourselves some time when we have tools like transfer learning.

"Transfer Learning will be the next driver of Machine Learning Success"- Andrew NG Recently, researchers from Google proposed the solution of a very fundamental question in the machine learning community -- What is being transferred in Transfer Learning? They explained various tools and analyses to address the fundamental question. The ability to transfer the domain knowledge of one machine in which it is trained on to another where the data is usually scarce is one of the desired capabilities for machines. Researchers around the globe have been using transfer learning in various deep learning applications, including object detection, image classification, medical imaging tasks, among others. Despite these utilisations, there are cases found by several researchers where there is a nontrivial difference in visual forms between the source and the target domain.

Switch studying's potential to retailer data gained whereas fixing an issue and apply it to a associated downside has attracted appreciable consideration. However regardless of latest breakthroughs, nobody totally understands what allows a profitable switch and which elements of algorithms are accountable for it. That's why Google researchers sought to develop evaluation strategies tailor-made to explainability challenges in switch studying. In a brand new paper, they are saying their contributions assist clear up just a few of the mysteries round why machine studying fashions switch efficiently -- or fail to. Through the first of a number of experiments within the research, the researchers sourced photographs from a medical imaging knowledge set of chest X-rays (CheXpert) and sketches, clip artwork, and work from the open supply DomainNet corpus.

Alzheimer's disease is estimated to affect around 50 million people worldwide and is rising rapidly, with a global economic burden of nearly a trillion dollars. This calls for scalable, cost-effective, and robust methods for detection of Alzheimer's dementia (AD). We present a novel architecture that leverages acoustic, cognitive, and linguistic features to form a multimodal ensemble system. It uses specialized artificial neural networks with temporal characteristics to detect AD and its severity, which is reflected through Mini-Mental State Exam (MMSE) scores. We first evaluate it on the ADReSS challenge dataset, which is a subject-independent and balanced dataset matched for age and gender to mitigate biases, and is available through DementiaBank. Our system achieves state-of-the-art test accuracy, precision, recall, and F1-score of 83.3% each for AD classification, and state-of-the-art test root mean squared error (RMSE) of 4.60 for MMSE score regression. To the best of our knowledge, the system further achieves state-of-the-art AD classification accuracy of 88.0% when evaluated on the full benchmark DementiaBank Pitt database. Our work highlights the applicability and transferability of spontaneous speech to produce a robust inductive transfer learning model, and demonstrates generalizability through a task-agnostic feature-space. The source code is available at https://github.com/wazeerzulfikar/alzheimers-dementia

Transfer learning is an area of intense AI research -- it focuses on storing knowledge gained while solving a problem and applying it to a related problem. But despite recent breakthroughs, it's not yet well-understood what enables a successful transfer and which parts of algorithms are responsible for it. That's why Google researchers sought to develop analysis techniques tailored to explainability challenges in transfer learning. In a new paper, they say their contributions help to solve a few of the mysteries around why machine learning models successfully -- or unsuccessfully -- transfer. During the first of several experiments in the course of the study, the researchers sourced images from a medical imaging data set of chest x-rays (CheXpert) and sketches, clip art, and paintings from the open source DomainNet corpus.

Transfer learning has become a common practice for training deep learning models with limited labeled data in a target domain. On the other hand, deep models are vulnerable to adversarial attacks. Though transfer learning has been widely applied, its effect on model robustness is unclear. To figure out this problem, we conduct extensive empirical evaluations to show that fine-tuning effectively enhances model robustness under white-box FGSM attacks. We also propose a black-box attack method for transfer learning models which attacks the target model with the adversarial examples produced by its source model. To systematically measure the effect of both white-box and black-box attacks, we propose a new metric to evaluate how transferable are the adversarial examples produced by a source model to a target model. Empirical results show that the adversarial examples are more transferable when fine-tuning is used than they are when the two networks are trained independently.

Author: fchollet Date created: 2020/04/15 Last modified: 2020/05/12 Description: Complete guide to transfer learning & fine-tuning in Keras. Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis. Transfer learning is usually done for tasks where your dataset has too little data to train a full-scale model from scratch. A last, optional step, is fine-tuning, which consists of unfreezing the entire model you obtained above (or part of it), and re-training it on the new data with a very low learning rate.

Transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. The traditional machine learning approach generalizes unseen data based on patterns learned from the training data, whereas for transfer learning, it begins from previously learned patterns to solve a different task. In this post, we shall focus on the pre-trained model approach as it is commonly used in the field of deep learning. A pre-trained model is a saved network that was previously trained on a large dataset, typically on a large-scale image-classification task. One can use the pre-trained model as it is or use transfer learning to customize this model to a given task.

Sensitivity to adversarial noise hinders deployment of machine learning algorithms in security-critical applications. Although many adversarial defenses have been proposed, robustness to adversarial noise remains an open problem. The most compelling defense, adversarial training, requires a substantial increase in processing time and it has been shown to overfit on the training data. In this paper, we aim to overcome these limitations by training robust models in low data regimes and transfer adversarial knowledge between different models. We train a meta-optimizer which learns to robustly optimize a model using adversarial examples and is able to transfer the knowledge learned to new models, without the need to generate new adversarial examples.

Sensitivity to adversarial noise hinders deployment of machine learning algorithms in security-critical applications. Although many adversarial defenses have been proposed, robustness to adversarial noise remains an open problem. The most compelling defense, adversarial training, requires a substantial increase in processing time and it has been shown to overfit on the training data. In this paper, we aim to overcome these limitations by training robust models in low data regimes and transfer adversarial knowledge between different models. We train a meta-optimizer which learns to robustly optimize a model using adversarial examples and is able to transfer the knowledge learned to new models, without the need to generate new adversarial examples. Experimental results show the meta-optimizer is consistent across different architectures and data sets, suggesting it is possible to automatically patch adversarial vulnerabilities.