XLDA: Linear Discriminant Analysis for Scaling Continual Learning to
  Extreme Classification at the Edge
Karan Shah Vishruth Veerendranath Anushka Hebbar Raghavendra Bhat
Streaming Linear Discriminant Analysis (LDA) while proven in
Class-incremental Learning deployments at the edge with limited classes (upto
1000), has not been proven for deployment in extreme classification scenarios.
In this paper, we present: (a) XLDA, a framework for Class-IL in edge
deployment where LDA classifier is proven to be equivalent to FC layer
including in extreme classification scenarios, and (b) optimizations to enable
XLDA-based training and inference for edge deployment where there is a
constraint on available compute resources. We show up to 42x speed up using a
batched training approach and up to 5x inference speedup with nearest neighbor
search on extreme datasets like AliProducts (50k classes) and Google Landmarks
V2 (81k classes)