Convolutional Tables Ensemble: classification in microseconds
We study classifiers operating under severe classification time constraints, corresponding to 1 1000 CPU microseconds, using Convolutional Tables Ensemble (CTE) an inherently fast architecture for object category recognition. The architecture is based on convolutionally applied sparse feature extraction, using trees or ferns, and a linear voting layer. Several structure and optimization variants are considered, including novel decision functions, tree learning algorithm, and distillation from CNN to CTE architecture. Accuracy improvements of 24 45% over related art of similar speed are demonstrated on standard object recognition benchmarks. Using Pareto speed accuracy curves, we show that CTE can provide better accuracy than Convolutional Neural Networks (CNN) for a certain range of classification time constraints, or alternatively provide similar error rates with 5 200X speedup.
ArXiv