This new formulation leads to an algorithm that produces a stochastic classifier by playing a two-player non-zero-sum game solving for what we call a semi-coarse correlated equilibrium, which in turn corresponds to an approximately optimal and feasible solution to the constrained optimization problem.
Submodular maximization has become established as the method of choice for the task of selecting representative and diverse summaries of data.
We present a general framework for solving a large class of learning problems with non-linear functions of classification rates.
However, this measure of performance conceals significant differences in how different classes and images are impacted by model compression techniques.
There has been remarkable progress on object detection and re-identification (re-ID) in recent years which are the key components of multi-object tracking.
Ranked #1 on Multi-Object Tracking on MOT16 (using extra training data)
Such architectural design and abstractions enable researchers and developers to extend the toolkit with their new algorithms and improvements, and to use it for performance benchmarking.
Third, we perform a large-scale empirical study and show that such pairs of observations are sufficient to reliably learn disentangled representations on several benchmark data sets.