Learning from data sequentially arriving, possibly in a non i.i.d. way, with changing task distribution over time is called continual learning. Much of the work thus far in continual learning focuses on supervised learning and some recent works on unsupervised learning. In many domains, each task contains a mix of labelled (typically very few) and unlabelled (typically plenty) training examples, which necessitates a semi-supervised learning approach. To address this in a continual learning setting, we propose a framework for semi-supervised continual learning called Meta-Consolidation for Continual Semi-Supervised Learning (MCSSL). Our framework has a hypernetwork that learns the meta-distribution that generates the weights of a semi-supervised auxiliary classifier generative adversarial network $(\textit{Semi-ACGAN})$ as the base network. We consolidate the knowledge of sequential tasks in the hypernetwork, and the base network learns the semi-supervised learning task. Further, we present $\textit{Semi-Split CIFAR-10}$, a new benchmark for continual semi-supervised learning, obtained by modifying the $\textit{Split CIFAR-10}$ dataset, in which the tasks with labelled and unlabelled data arrive sequentially. Our proposed model yields significant improvements in the continual semi-supervised learning setting. We compare the performance of several existing continual learning approaches on the proposed continual semi-supervised learning benchmark of the Semi-Split CIFAR-10 dataset.

Most of the time people start their machine learning journey with few basic techniques in which Unsupervised Learning, Supervised Learning, and Reinforcement Learning is the major ones. For any effective business operations, good use of information plays a vital role. However, at some point, the information goes beyond simple processing capacity. For that matter, machine learning plays its part. Before anything happens, information needs to be explored and certain processing needs to be done on it.

We look into Generative Adversarial Network (GAN), its prevalent variants and applications in a number of sectors. GANs combine two neural networks that compete against one another using zero-sum game theory, allowing them to create much crisper and discrete outputs. GANs can be used to perform image processing, video generation and prediction, among other computer vision applications. GANs can also be utilised for a variety of science-related activities, including protein engineering, astronomical data processing, remote sensing image dehazing, and crystal structure synthesis. Other notable fields where GANs have made gains include finance, marketing, fashion design, sports, and music. Therefore in this article we provide a comprehensive overview of the applications of GANs in a wide variety of disciplines. We first cover the theory supporting GAN, GAN variants, and the metrics to evaluate GANs. Then we present how GAN and its variants can be applied in twelve domains, ranging from STEM fields, such as astronomy and biology, to business fields, such as marketing and finance, and to arts, such as music. As a result, researchers from other fields may grasp how GANs work and apply them to their own study. To the best of our knowledge, this article provides the most comprehensive survey of GAN's applications in different fields.

Semi-supervised learning is a machine learning technique of deriving useful information from both labelled and unlabelled data. Before doing this tutorial, you should have basic familiarity with supervised learning on images with PyTorch. We will omit reinforcement learning here and concentrate on the first two types. In supervised learning, our data consists of labelled objects. A machine learning model is tasked with learning how to assign labels (or values) to objects.

Machine Learning (ML) has bought significant promises in different fields in both academia and industry. Day by day, ML has grown its engagement in a comprehensive list of applications such as image, speech recognition, pattern recognition, optimization, natural language processing, and recommendations, and so many others. Programming computers to learn from experience should eventually eliminate the need for much of this detailed programming effort. Machine Learning can be divided into four main techniques: regression, classification, clustering, and reinforcement learning. Those techniques solve problems with different natures in mainly two forms: supervised and unsupervised learning.

Machine Learning can be defined as an approach to achieve artificial intelligence through systems or software models that can learn from experience to find patterns in a set of data. Google uses artificial intelligence and machine learning in almost all of its applications. Google Photos display photos related to your search terms and animate similar photos from your albums into quick videos. Gmail suggest phrases and complete sentences in emails. Google Assistant can take over real-world tasks such as booking a haircut appointment over phone.

Raven's Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans. In this paper, to study the abstract reasoning capability of deep neural networks, we propose the first unsupervised learning method for solving RPM problems. Since the ground truth labels are not allowed, we design a pseudo target based on the prior constraints of the RPM formulation to approximate the ground truth label, which effectively converts the unsupervised learning strategy into a supervised one. However, the correct answer is wrongly labelled by the pseudo target, and thus the noisy contrast will lead to inaccurate model training. To alleviate this issue, we propose to improve the model performance with negative answers. Moreover, we develop a decentralization method to adapt the feature representation to different RPM problems. Extensive experiments on three datasets demonstrate that our method even outperforms some of the supervised approaches. Our code is available at https://github.com/visiontao/ncd.

Genetics: DNA(Deoxyribonucleic acid) is a double helix that carries genetic info of development, functioning, growth, and reproduction of all organisms and viruses too! Each and Every infant inherits genes from their biological parents. And the study of these genes is Genetics. Most of us have two copies of the genome (contains genes as well as Noncoding DNA, the study of this is genomics) with 6Billion pairs of DNA! In order to reach our desired requirements, we must have an approach or methods to achieve it. Machine Learning essentially has three such methods in order to tackle the maximum number of our requirements.

Federated Semi-Supervised Learning (FedSSL) has gained rising attention from both academic and industrial researchers, due to its unique characteristics of co-training machine learning models with isolated yet unlabeled data. Most existing FedSSL methods focus on the classical scenario, i.e, the labeled and unlabeled data are stored at the client side. However, in real world applications, client users may not provide labels without any incentive. Thus, the scenario of labels at the server side is more practical. Since unlabeled data and labeled data are decoupled, most existing FedSSL approaches may fail to deal with such a scenario. To overcome this problem, in this paper, we propose FedCon, which introduces a new learning paradigm, i.e., contractive learning, to FedSSL.

The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction (OpenRE). However, high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters cannot explicitly align with the relational semantic classes. In this work, we propose a relation-oriented clustering model and use it to identify the novel relations in the unlabeled data. Specifically, to enable the model to learn to cluster relational data, our method leverages the readily available labeled data of pre-defined relations to learn a relation-oriented representation. We minimize distance between the instance with same relation by gathering the instances towards their corresponding relation centroids to form a cluster structure, so that the learned representation is cluster-friendly. To reduce the clustering bias on predefined classes, we optimize the model by minimizing a joint objective on both labeled and unlabeled data. Experimental results show that our method reduces the error rate by 29.2% and 15.7%, on two datasets respectively, compared with current SOTA methods.