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Advancing to 3D Deep Neural Networks in Medical Image Analysis
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,...
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GPipe – Training Giant Neural Nets using Pipeline Parallelism
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,...
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Struct2Depth – Predicting object depth in dynamic environments
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,...
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SlowFast – Dual-mode CNN for Video Understanding
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...
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Style-based GANs – Generating and Tuning Realistic Artificial Faces
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...
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InstaGAN – Instance-aware Image-to-image Translation – Using GANs for Object...
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,...
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BagNet – Solving ImageNet with a Simple Bag-of-features Model
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...
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