Learning Deep Nearest Neighbor Representations Using Differentiable Boundary Trees

Understand

Nearest neighbor (kNN) methods have been gaining popularity in recent years in light of advances in hardware and efficiency of algorithms.

• There is a plethora of methods to choose from today, each with their own advantages and disadvantages.
• One requirement shared between all kNN based methods is the need for a good representation and distance measure between samples.
• We introduce a new method called differentiable boundary tree which allows for learning deep kNN representations.