GANs involve training two networks in an adversarial game, where each network's task depends on its adversary. Recently, several works have framed GAN training as an online or continual learning problem [1-6]. We focus on the discriminator, which must perform classification under an (adversarially) shifting data distribution. When trained on sequential tasks, neural networks exhibit forgetting. For GANs, discriminator forgetting leads to training instability [1]. To counter forgetting, we encourage the discriminator to maintain useful representations by adding a self-supervision. Conditional GANs have a similar effect using labels. However, our self-supervised GAN does not require labels, and closes the performance gap between conditional and unconditional models. We show that, in doing so, the self-supervised discriminator learns better representations than regular GANs.

Despite the widespread usage of deep learning in applications (Mnih et al., 2015; Kalchbrenner et al., 2017; Silver et al., 2017; van den Oord et al., 2018), current theoretical understanding of deep networks continues to lag behind the pursued engineering outcomes. Recent theoretical contributions have considered networks in their randomized initial state, or made strong assumptions about the parameters or data during training. For example, Cho & Saul (2009); Daniely et al. (2016); Bach (2017); Tsuchida et al. (2018) analyze the kernels of neural networks with random IID distributions. Insightful analysis connecting signal propagation in deep networks to chaos have made similar assumptions (Poole et al., 2016; Raghu et al., 2017). Clearly the assumption of random IID weights is only valid when the network is in its random initial state.

In this paper, we propose a fully supervised speaker diarization approach, named unbounded interleaved-state recurrent neural networks (UIS-RNN). Given extracted speaker-discriminative embeddings (a.k.a. d-vectors) from input utterances, each individual speaker is modeled by a parameter-sharing RNN, while the RNN states for different speakers interleave in the time domain. This RNN is naturally integrated with a distance-dependent Chinese restaurant process (ddCRP) to accommodate an unknown number of speakers. Our system is fully supervised and is able to learn from examples where time-stamped speaker labels are annotated. We achieved a 7.6% diarization error rate on NIST SRE 2000 CALLHOME, which is better than the state-of-the-art method using spectral clustering. Moreover, our method decodes in an online fashion while most state-of-the-art systems rely on offline clustering.

Saliency methods have emerged as a popular tool to highlight features in an input deemed relevant for the prediction of a learned model. Several saliency methods have been proposed, often guided by visual appeal on image data. In this work, we propose an actionable methodology to evaluate what kinds of explanations a given method can and cannot provide. We find that reliance, solely, on visual assessment can be misleading. Through extensive experiments we show that some existing saliency methods are independent both of the model and of the data generating process. Consequently, methods that fail the proposed tests are inadequate for tasks that are sensitive to either data or model, such as, finding outliers in the data, explaining the relationship between inputs and outputs that the model learned, and debugging the model. We interpret our findings through an analogy with edge detection in images, a technique that requires neither training data nor model. Theory in the case of a linear model and a single-layer convolutional neural network supports our experimental findings.

ABSTRACT In order to save the memory, we propose a miniaturization method for neural network to reduce the parameter quantity existed in remote sensing (RS) image semantic segmentation model. The compact convolution optimization method is first used for standard U-Net to reduce the weights quantity. With the purpose of decreasing model performance loss caused by miniaturization and based on the characteristics of remote sensing image, fewer down-samplings and improved cascade atrous convolution are then used to improve the performance of the miniaturized U-Net. Compared with U-Net, our proposed Micro-Net not only achieves 29.26 times model compression, but also basically maintains the performance unchanged on the public dataset. Index Terms--semantic segmentation, compact convolution, atrous convolution, deep learning 1. INTRODUCTION As the major data source in mapping [1], earth observation [2], ground target recognition [3], RS images have important research value.

Krystian Woznicki (KW):'Rethinking political agency in an AI-driven world' is the topic of the AMBIENT REVOLTS conference in Berlin on 8–10 November. I would therefore like to begin by asking you about the deployment of algorithms at state borders. You have noted that'in order to learn, to change daily and evolve [they] require precisely the circulations and mobilities that pass through'. This observation is part of your larger argument about how governmentality is less concerned with prohibiting movement than with facilitating it in productive ways. The role of self-learning algorithms would seem to be very significant in this context, since – like capitalism – they also hinge upon movement. When it comes to their thirst for traffic, how do you think that relationship between self-learning algorithms and capitalism? Louise Amoore (LA): Yes, I agree that the role of'self learning' or semi-supervised algorithms is of the utmost relevance in understanding how movement and circulation matters.

All the code and online demo are available at the project page. Describing an image is easy for humans, and we are able to do it from a very young age. In machine learning, this task is a discriminative classification/regression problem, i.e. predicting feature labels from input images. Recent advancements in ML/AI techniques, especially deep learning models, are beginning to excel in these tasks, sometimes reaching or exceeding human performance, as is demonstrated in scenarios like visual object recognition (e.g. from AlexNet to ResNet on ImageNet classification) and object detection/segmentation (e.g. from RCNN to YOLO on COCO dataset), etc. However, the other way around, generating realistic images based on descriptions, is much harder, and takes years of graphic design training.

The late Stephen Hawkins worried that AI could end mankind. Elon Musk warned machines that learned to operate without a human telling them what to do could "destroy humanity as a matter of course without even thinking about it" if it "[had] a goal and humanity just happens [to be] in the way.". But reality has proven that while AI can beat humans at games, it still fails at common tasks an infant can do, such as holding an object. In fact, to solve this problem, researchers from OpenAI used 6144 CPUs and 8 GPUs to collect about one hundred years of experience and trained the AI for 50 hours. As a result, the robotic hand can handle unknown objects -- as long as they are "within reason."

I'll show you where to begin. A lot of bullshit guides and articles tell you do this course or that certificate, and that's fine for foundational learning, but the true road to understanding at an elite level is building something cool and new. Building things and sharing them with the public forces you to introduce a structure, rigor, and integrity to your work, and allows you to truly grasp the concepts that are at play. The purpose of this article is to elucidate areas and resources you can start with to really build novel ml projects, tools, and contributions. If you'd like to gain a mathematically rigorous, fundamental understanding of Machine Learning and Deep Learning, check out this post.

Imagine you could know the mood of the people on the Internet. Maybe you are not interested in its entirety, but only if people are today happy on your favorite social media platform. After this tutorial, you'll be equipped to do this. While doing this, you will get a grasp of current advancements of (deep) neural networks and how they can be applied to text. Reading the mood from text with machine learning is called sentiment analysis, and it is one of the prominent use cases in text classification. This falls into the very active research field of natural language processing (NLP). Other common use cases of text classification include detection of spam, auto tagging of customer queries, and categorization of text into defined topics. So how can you do this? Free Bonus: 5 Thoughts On Python Mastery, a free course for Python developers that shows you the roadmap and the mindset you'll need to take your Python skills to the next level. Before we start, let's take a look at what data we have. Go ahead and download the data set from the Sentiment Labelled Sentences Data Set from the UCI Machine Learning Repository. By the way, this repository is a wonderful source for machine learning data sets when you want to try out some algorithms. This data set includes labeled reviews from IMDb, Amazon, and Yelp. Each review is marked with a score of 0 for a negative sentiment or 1 for a positive sentiment. With this data set, you are able to train a model to predict the sentiment of a sentence. Take a quick moment to think about how you would go about predicting the data.