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Data augmentation has, for a long while, been serving as a means of replacing a "static" dataset with transformed variants, bolstering the invariance of Convolutional Neural Networks (CNNs), and usually leading to robustness to input. Note: Invariance boils down to making models blind to certain perturbations, when making...
David Landup
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". From it, keypoint detection (oftentimes used for pose estimation) was extracted. Object detection and keypoint detection aren't as standardized as image classification, mainly because most of...
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". Object detection isn't as standardized as image classification, mainly because most of the new developments are typically done by individual researchers, maintainers and developers, rather than...
Object detection is a large field in computer vision, and one of the more important applications of computer vision "in the wild". On one end, it can be used to build autonomous systems that navigate agents through environments - be it robots performing tasks or self-driving cars, but...
Most practitioners, while first learning about Convolutional Neural Network (CNN) architectures - learn that it's comprised of three basic segments: Convolutional Layers Pooling Layers Fully-Connected Layers Most resources have some variation on this segmentation, including my own book. Especially online - fully-connected layers refer to a flattening layer and (usually)...
Deep Learning frameworks like Keras lower the barrier to entry for the masses and democratize the development of DL models to unexperienced folk, who can rely on reasonable defaults and simplified APIs to bear the brunt of heavy lifting, and produce decent results. A common confusion arises between newer deep...
There are plenty of guides explaining how transformers work, and for building an intuition on a key element of them - token and position embedding. Positionally embedding tokens allowed transformers to represent non-rigid relationships between tokens (usually, words), which is much better at modeling our context-driven speech in language modeling....
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