Deep Learning Explained
Prior to 2012, most machine learning algorithms were statistical models which used hand-created features. The models were highly explainable and somewhat effective but failed to reach a high accuracy in many language and computer vision tasks. In 2012, AlexNet, a deep neural network model, won the 2012 ImageNet competition by a large margin, and ignited […]
Generative Adversarial Networks (GANs) have been used for many image processing tasks, among them, generating images from scratch (Style-based GANs) and applying new styles to images. A new paper, named InstaGAN, presents an innovative use of GANs – transfiguring instances of a given object in an image into another object while preserving the rest of […]
Generative Adversarial Networks (GAN) are a relatively new concept in Machine Learning, introduced for the first time in 2014. Their goal is to synthesize artificial samples, such as images, that are indistinguishable from authentic images. A common example of a GAN application is to generate artificial face images by learning from a dataset of celebrity […]
Detecting objects in images and categorizing them is one of the more well-known Computer Vision tasks, popularized by the 2010 ImageNet dataset and challenge. While much progress has been achieved on ImageNet, a still vexing task is video understanding – analyzing a video segment and explaining what’s happening inside of it. Despite some recent progress […]
While recent advances in computer vision are helping robots and autonomous vehicles navigate complex environments effectively, some challenges still remain. One major challenge is depth prediction, i.e. the ability of a moving robot to recognize the depth of objects around it, a requirement for it to navigate a real-life environment safely. Historically, the most effective […]
In recent years the size of machine learning datasets and models has been constantly increasing, allowing for improved results on a wide range of tasks. At the same time hardware acceleration (GPUs, TPUs) has also been improving but at a significantly slower pace. The gap between model growth and hardware improvement has increased the importance […]
For several decades computer scientists have been attempting to build medical software to help physicians analyze medical images. Until 2012, when deep neural networks first proved their effectiveness, most attempts included extensive feature engineering tailored to specific types of medical images, and were usually low-quality and therefore ineffective in helping doctors in practice. In recent […]
