With the of advent rich classification models and high computational power
visual recognition systems have found many operational applications.
Recognition in the real world poses multiple challenges that are not apparent
in controlled lab environments. The datasets are dynamic and novel categories
must be continuously detected and then added. At prediction time, a trained
system has to deal with myriad unseen categories. Operational systems require
minimum down time, even to learn. To handle these operational issues, we
present the problem of Open World recognition and formally define it. We prove
that thresholding sums of monotonically decreasing functions of distances in
linearly transformed feature space can balance "open space risk" and empirical
risk. Our theory extends existing algorithms for open world recognition. We
present a protocol for evaluation of open world recognition systems. We present
the Nearest Non-Outlier (NNO) algorithm which evolves model efficiently, adding
object categories incrementally while detecting outliers and managing open
space risk. We perform experiments on the ImageNet dataset with 1.2M+ images to
validate the effectiveness of our method on large scale visual recognition
tasks. NNO consistently yields superior results on open world recognition.

通过对线性变换特征空间距离单调递减函数的和进行分组来解决 “开放空间风险” 和经验风险之间的平衡，提出了开放世界识别问题，提出了一种用于评估开放世界识别系统的协议，并演示 Nearest Non-Outlier 算法如何可靠有效地处理大规模的图像数据集。