Multi-view classification is inspired by the behavior of humans, especially when fine-grained features or in our case rarely occurring anomalies are to be detected. Current contributions point to the problem of how high-dimensional data can be fused. In this work, we build upon the deep support vector data description algorithm and address multi-perspective anomaly detection using three different fusion techniques i.e. early fusion, late fusion, and late fusion with multiple decoders. We employ different augmentation techniques with a denoising process to deal with scarce one-class data, which further improves the performance (ROC AUC = 80\%). Furthermore, we introduce the dices dataset that consists of over 2000 grayscale images of falling dices from multiple perspectives, with 5\% of the images containing rare anomalies (e.g. drill holes, sawing, or scratches). We evaluate our approach on the new dices dataset using images from two different perspectives and also benchmark on the standard MNIST dataset. Extensive experiments demonstrate that our proposed approach exceeds the state-of-the-art on both the MNIST and dices datasets. To the best of our knowledge, this is the first work that focuses on addressing multi-perspective anomaly detection in images by jointly using different perspectives together with one single objective function for anomaly detection.

Distributional semantics has deeply changed in the last decades. First, predict models stole the thunder from traditional count ones, and more recently both of them were replaced in many NLP applications by contextualized vectors produced by Transformer neural language models. Although an extensive body of research has been devoted to Distributional Semantic Model (DSM) evaluation, we still lack a thorough comparison with respect to tested models, semantic tasks, and benchmark datasets. Moreover, previous work has mostly focused on task-driven evaluation, instead of exploring the differences between the way models represent the lexical semantic space. In this paper, we perform a comprehensive evaluation of type distributional vectors, either produced by static DSMs or obtained by averaging the contextualized vectors generated by BERT. First of all, we investigate the performance of embeddings in several semantic tasks, carrying out an in-depth statistical analysis to identify the major factors influencing the behavior of DSMs. The results show that i.) the alleged superiority of predict based models is more apparent than real, and surely not ubiquitous and ii.) static DSMs surpass contextualized representations in most out-of-context semantic tasks and datasets. Furthermore, we borrow from cognitive neuroscience the methodology of Representational Similarity Analysis (RSA) to inspect the semantic spaces generated by distributional models. RSA reveals important differences related to the frequency and part-of-speech of lexical items.

Deep Reinforcement learning holds the guarantee of empowering self-ruling robots to master enormous collections of conduct abilities with negligible human mediation. The improvements brought by this technique enables robots to perform difficult tasks such as grabbing or reaching targets. Nevertheless, the training process is still time consuming and tedious especially when learning policies only with RGB camera information. This way of learning is capital to transfer the task from simulation to the real world since the only external source of information for the robot in real life is video. In this paper, we study how to limit the time taken for training a robotic arm with 6 Degrees Of Freedom (DOF) to reach a ball from scratch by assembling a pipeline as efficient as possible. The pipeline is divided into two parts: the first one is to capture the relevant information from the RGB video with a Computer Vision algorithm. The second one studies how to train faster a Deep Reinforcement Learning algorithm in order to make the robotic arm reach the target in front of him. Follow this link to find videos and plots in higher resolution: \url{https://drive.google.com/drive/folders/1_lRlDSoPzd_GTcVrxNip10o_lm-_DPdn?usp=sharing}

There has been a rise in the use of Machine Learning as a Service (MLaaS) Vision APIs as they offer multiple services including pre-built models and algorithms, which otherwise take a huge amount of resources if built from scratch. As these APIs get deployed for high-stakes applications, it's very important that they are robust to different manipulations. Recent works have only focused on typical adversarial attacks when evaluating the robustness of vision APIs. We propose two new aspects of adversarial image generation methods and evaluate them on the robustness of Google Cloud Vision API's optical character recognition service and object detection APIs deployed in real-world settings such as sightengine.com, picpurify.com, Google Cloud Vision API, and Microsoft Azure's Computer Vision API. Specifically, we go beyond the conventional small-noise adversarial attacks and introduce secret embedding and transparent adversarial examples as a simpler way to evaluate robustness. These methods are so straightforward that even non-specialists can craft such attacks. As a result, they pose a serious threat where APIs are used for high-stakes applications. Our transparent adversarial examples successfully evade state-of-the art object detections APIs such as Azure Cloud Vision (attack success rate 52%) and Google Cloud Vision (attack success rate 36%). 90% of the images have a secret embedded text that successfully fools the vision of time-limited humans but is detected by Google Cloud Vision API's optical character recognition. Complementing to current research, our results provide simple but unconventional methods on robustness evaluation.

At the Google I/O event, the company has announced a revamped cloud-based machine learning platform branded as Vertex AI. It's unusual for Google to announce cloud-related services at Google I/O, a forum for launching consumer and developer technologies. Since Cloud Next - the flagship cloud user conference - is postponed to October, Vertex AI found its place in I/O announcements. This is the third iteration of the Google Cloud ML platform since its original launch. What prompted Google to launch Vertex AI? Let's find out.

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation [1]. Its strength comes from the fact that it learns the mapping directly from input text to associated output text. It has been proven to be more effective than traditional phrase-based machine translation, which requires much more effort to design the model. On the other hand, NMT models are costly to train, especially on large-scale translation datasets. They are also significantly slower at inference time due to the large number of parameters used.

Technological innovations have changed our lives in a way no one had ever imagined. Considering top technologies, mainly artificial intelligence, it would be no wrong to say that technology has revolutionized the way we used to work, shop, communicate, and travel. Today we have AI-powered software and applications that can act as human beings to resolve our queries and provide us with the information we need. Quytech, the leading mobile and web app development company, explores the potential of artificial intelligence and develops AI-based solutions that can help businesses to grow and succeed. Artificial intelligence relies on data available on the world wide web to assist in decision making.

The phrase artificial intelligence doesn't have any fixed meaning, for it was coined by Dartmouth professor John McCarthy in 1955 as a placeholder in a grant application. The best summary so far is by MIT's Marvin Minsky, a colleague of McCarthy's, who said the term stands for whatever is at the cutting edge of computer science. Commercial entities, such as software makers, will often use the term to mean whatever they want, simply to sound impressive by gaining the imprimatur of having "AI." Deep learning is a subset of a wider field of AI software called machine learning. Some believe AI has to have an element of learning because all intelligent entities exhibit an ability to learn.

The acceleration of digital transformation and technology adoption have benefited many industries. It has given rise to many innovative technologies and deepfakes are one of them. We all saw how Barack Obama called Donald Trump a'complete dipshit'. This is an example of deepfake videos. Deepfake technology uses AI, Deep Learning, and a Generative Adversarial Network or GAN to build videos or images that seem real but are actually fake.

The PyTorch training is tailored by our leading industry expert faculty having substantial experience in this domain. This course is an open-source machine learning library from Python. PyTorch is also a deep learning framework that is used to create machine learning workflows. This training will help you master the concepts of ML workflow. This training program is in line with the best and latest information and practices of using PyTorch efficiently.