Before we start, if you are reading this article, I am sure that we share similar interests and are/will be in similar industries. So let's connect via Linkedin! Please do not hesitate to send a contact request! If you have been trying to build machine learning models with high accuracy; but never tried Transfer Learning, this article will change your life. At least, it did mine!

While many deep learning methods have seen significant success in tackling the problem of domain adaptation and few-shot learning separately, far fewer methods are able to jointly tackle both problems in Cross-Domain Few-Shot Learning (CD-FSL). This problem is exacerbated under sharp domain shifts that typify common computer vision applications. In this paper, we present a novel, flexible and effective method to address the CD-FSL problem. Our method, called Score-based Meta Transfer-Learning (SB-MTL), combines transfer-learning and meta-learning by using a MAML-optimized feature encoder and a score-based Graph Neural Network. First, we have a feature encoder with specific layers designed to be fine-tuned. To do so, we apply a first-order MAML algorithm to find good initializations. Second, instead of directly taking the classification scores after fine-tuning, we interpret the scores as coordinates by mapping the pre-softmax classification scores onto a metric space. Subsequently, we apply a Graph Neural Network to propagate label information from the support set to the query set in our score-based metric space. We test our model on the Broader Study of Cross-Domain Few-Shot Learning (BSCD-FSL) benchmark, which includes a range of target domains with highly varying dissimilarity to the miniImagenet source domain. We observe significant improvements in accuracy across 5, 20 and 50 shot, and on the four target domains. In terms of average accuracy, our model outperforms previous transfer-learning methods by 5.93% and previous meta-learning methods by 14.28%.

Transfer learning is an important topic. As a civilization, we have been passing on the knowledge from one generation to the other, enabling the technological advancement that we enjoy today. It's the edifice that supports most of the state-of-the-art models that are blowing steam, empowering many services that we take for granted. Transfer learning is about having a good starting point for the downstream task we're interested in solving. In this article, we're going to discuss how to piggyback on transfer learning to get a warm start to solve an image classification task. The content of this article is based on "TensorFlow 2 in Action" by Manning and on TensorFlow 2.2.

The concept of transfer learning lies in imparting knowledge learned for performing a task to another task that is different but similar. How is Transfer Learning Useful to Me? In the context of humans, transfer learning is crucial to our lives. Let us use the CIFAR-10 dataset that contains 10 categories of images -- airplane, automobile, bird, cat, deer, dog, frog, horse, ship and truck. Our task of interest is to classify every image to its corresponding category.

We propose a unified multi-tasking framework to represent the complex and uncertain causal process of financial market dynamics, and then to predict the movement of any type of index with an application on the monthly direction of the S&P500 index. our solution is based on three main pillars: (i) the use of transfer learning to share knowledge and feature (representation, learning) between all financial markets, increase the size of the training sample and preserve the stability between training, validation and test sample. (ii) The combination of multidisciplinary knowledge (Financial economics, behavioral finance, market microstructure and portfolio construction theories) to represent a global top-down dynamics of any financial market, through a graph. (iii) The integration of forward looking unstructured data, different types of contexts (long, medium and short term) through latent variables/nodes and then, use a unique VAE network (parameter sharing) to learn simultaneously their distributional representation. We obtain Accuracy, F1-score, and Matthew Correlation of 74.3 %, 67 % and 0.42 above the industry and other benchmark on 12 years test period which include three unstable and difficult sub-period to predict.

We present a novel approach of 2D to 3D transfer learning based on mapping pre-trained 2D convolutional neural network weights into planar 3D kernels. The method is validated by the proposed planar 3D res-u-net network with encoder transferred from the 2D VGG-16, which is applied for a single-stage unbalanced 3D image data segmentation. In particular, we evaluate the method on the MICCAI 2016 MS lesion segmentation challenge dataset utilizing solely fluid-attenuated inversion recovery (FLAIR) sequence without brain extraction for training and inference to simulate real medical praxis. The planar 3D res-u-net network performed the best both in sensitivity and Dice score amongst end to end methods processing raw MRI scans and achieved comparable Dice score to a state-of-the-art unimodal not end to end approach. Complete source code was released under the open-source license, and this paper complies with the Machine learning reproducibility checklist.

In traditional Machine Learning domains, we usually take a huge dataset which is specific to a particular task and wish to train a model for regression/classification purposes using this dataset. That's radically far from how humans take advantage of their past experiences to learn very quickly a new task from only a handset of examples. Meta-Learning is essentially learning to learn. Formally, it can be defined as using metadata of an algorithm or a model to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself. Each learning algorithm is based on a set of assumptions about the data, which is called its inductive bias.

Cognitive science suggests that the self-representation is critical for learning and problem-solving. However, there is a lack of computational methods that relate this claim to cognitively plausible robots and reinforcement learning. In this paper, we bridge this gap by developing a model that learns bidirectional action-effect associations to encode the representations of body schema and the peripersonal space from multisensory information, which is named multimodal BidAL. Through three different robotic experiments, we demonstrate that this approach significantly stabilizes the learning-based problem-solving under noisy conditions and that it improves transfer learning of robotic manipulation skills.

Preview this course - GET COUPON CODE Latest update: I will show you both how to use a pretrained model and how to train one yourself with a custom dataset on Google Colab. This course is a complete guide for setting up TensorFlow object detection api, Transfer learning and a lot more I think what you'll find is that, this course is so entirely different from the previous one, you will be impressed at just how much material we have to cover. Here is the details about the project. Here we will star from colab understating because that will help to use free GPU provided by google to train up our model. We're going to bridge the gap between the basic CNN architecture you already know and love, to modern, novel architectures such as ResNet, and Inception.

Transfer learning is a useful machine learning framework that allows one to build task-specific models (student models) without significantly incurring training costs using a single powerful model (teacher model) pre-trained with a large amount of data. The teacher model may contain private data, or interact with private inputs. We investigate if one can leak or infer such private information without interacting with the teacher model directly. We describe such inference attacks in the context of face recognition, an application of transfer learning that is highly sensitive to personal privacy. Under black-box and realistic settings, we show that existing inference techniques are ineffective, as interacting with individual training instances through the student models does not reveal information about the teacher. We then propose novel strategies to infer from aggregate-level information. Consequently, membership inference attacks on the teacher model are shown to be possible, even when the adversary has access only to the student models. We further demonstrate that sensitive attributes can be inferred, even in the case where the adversary has limited auxiliary information. Finally, defensive strategies are discussed and evaluated. Our extensive study indicates that information leakage is a real privacy threat to the transfer learning framework widely used in real-life situations.