Data is the reason AV companies are racking up miles and miles of testing experience on public roads, recording and stockpiling petabytes of road lore. Waymo, for example, claimed in July more than 10 million miles in the real world and 10 billion miles in simulation. But here's yet another question the industry does not like to ask: Assume that AV companies have already collected petabytes or even exabytes of data on real roads. How much of that dataset has been labeled? Perhaps more important, how accurate is the data that's been annotated?

Cybersecurity, a huge industry worth over $100 billion, is regularly subject to buzzwords. Cybersecurity companies often (pretend) to use new state-of-the-art technologies to attract customers and sell their solutions. Naturally, with artificial intelligence being in one of its craziest hype cycles, we're seeing plenty of solutions that claim to use machine learning, deep learning and other AI-related technologies to automatically secure the networks and digital assets of their clients. But contrary to what many companies profess, machine learning is not a silver bullet that will automatically protect individuals and organizations against security threats, says Ilia Kolochenko, CEO of ImmuniWeb, a company that uses AI to test the security of web and mobile applications. While machine learning and other AI techniques will help improve the speed and quality of cybersecurity solutions, they will not be a replacement for many of the basic practices that companies often neglect.

In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available. Endowed with end-to-end structure, LLP-GAN performs approximation in the light of an adversarial learning mechanism, without imposing restricted assumptions on distribution. Accordingly, we can directly induce the final instance-level classifier upon the discriminator. Under mild assumptions, we give the explicit generative representation and prove the global optimality for LLP-GAN. Additionally, compared with existing methods, our work empowers LLP solver with capable scalability inheriting from deep models. Several experiments on benchmark datasets demonstrate vivid advantages of the proposed approach.

Recently, consistency-based methods have achieved state-of-the-art results in semi-supervised learning (SSL). These methods always involve two roles, an explicit or implicit teacher model and a student model, and penalize predictions under different perturbations by a consistency constraint. However, the weights of these two roles are tightly coupled since the teacher is essentially an exponential moving average (EMA) of the student. In this work, we show that the coupled EMA teacher causes a performance bottleneck. To address this problem, we introduce Dual Student, which replaces the teacher with another student. We also define a novel concept, stable sample, following which a stabilization constraint is designed for our structure to be trainable. Further, we discuss two variants of our method, which produce even higher performance. Extensive experiments show that our method improves the classification performance significantly on several main SSL benchmarks. Specifically, it reduces the error rate of the 13-layer CNN from 16.84% to 12.39% on CIFAR-10 with 1k labels and from 34.10% to 31.56% on CIFAR-100 with 10k labels. In addition, our method also achieves a clear improvement in domain adaptation.

Taken from: Are GANs Created Equal?

High-quality textures are an important component in many applications ranging from architectural rendering to game design and often require the work of skilled artists. The synthesis of natural textures is therefore an important problem, but the complexity of texture synthesis increases exponentially with the target size of the output texture. Recent advances in the field of Generative Adversarial Networks allow for novel techniques in this field with unprecedented visual quality. TileGAN tackles the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We propose an algorithm to combine outputs of GANs to produce high-resolution texture maps with virtually no boundary artifacts.

I was working on creating embeddings for medical concepts. These terms/phrases are used for annotating biomedical documents. Now usually the method of creating a co-occurrence matrix and then factorizing it to obtain dense, lower-dimensional vectors is termed as unsupervised learning since annotated data is not involved. I am using the same process but for the annotations themselves. Does this qualify as supervised learning since I need annotated data or does this qualify as unsupervised learning since the method of obtaining the embeddings is unsupervised?

The results from these contests come in many practical forms. They can be used to enhance service to customers, to create a virtual marketing "influencer", and to create art. Recently, GAN-created art sold at Christie's Inc. auction house for over $400,000!3 On the business front, marketers are turning to synthesized personas to serve as their social media "influencer", since it allows them to more-effectively control messaging to their target audience.4 In the app economy, GANs can be used to enhance facial authentication.

Semi-supervised learning is a setting in which one has labeled and unlabeled data available. In this survey we explore different types of theoretical results when one uses unlabeled data in classification and regression tasks. Most methods that use unlabeled data rely on certain assumptions about the data distribution. When those assumptions are not met in reality, including unlabeled data may actually decrease performance. Studying such methods, it therefore is particularly important to have an understanding of the underlying theory. In this review we gather results about the possible gains one can achieve when using semi-supervised learning as well as results about the limits of such methods. More precisely, this review collects the answers to the following questions: What are, in terms of improving supervised methods, the limits of semi-supervised learning? What are the assumptions of different methods? What can we achieve if the assumptions are true? Finally, we also discuss the biggest bottleneck of semi-supervised learning, namely the assumptions they make.

This work is a study of the impact of multiple aspects in a classic unsupervised word sense disambiguation algorithm. We identify relevant factors in a decision rule algorithm, including the initial labeling of examples, the formalization of the rule confidence, and the criteria for accepting a decision rule. Some of these factors are only implicitly considered in the original literature. We then propose a lightly supervised version of the algorithm, and employ a pseudo-word-based strategy to evaluate the impact of these factors. The obtained performances are comparable with those of highly optimized formulations of the word sense disambiguation method.