Cory's Wiki

YOLO methods apply a direct discriminative classification approach to object detection.

Attempts to replicate human brain behavior and structure in neural networks are heavily inspired by neuroscience. These efforts are characterized by an emphasis on very large networks (inspired by the massive $~10^{11}$ neurons in a human brain) and unsupervised processing of larger unorganized data sets¹⁾.

Whereas neural networks are all historically inspired by biology, these methods are more closely derived from existing biological neuroscience concepts.

In autoencoders, each feature is restricted to connect to a small subset of the lower layer. Inspired by biological neurons, local receptive fields support sparse coding and help enable many unsupervised methods and utilization of greater parallelization and GPU processing²⁾ e.g. text classification³⁾.

Feature Learning refers to a family of techniques which learn to replicate, approximate, or otherwise represent an input distribution as efficiently as possible. There are analogies and historic influence from compression, PCA, and Sparse Coding. Feature learning techniques include:

• Sparse Filtering
• Autoencoders
• RBMs