Deep learners tend to perform well when trained under the closed set
assumption but struggle when deployed under open set conditions. This motivates
the field of Open Set Recognition in which we seek to give deep learners the
ability to recognize whether a data sample belongs to the known classes trained
on or comes from the surrounding infinite world. Existing open set recognition
methods typically rely upon a single function for the dual task of
distinguishing between knowns and unknowns as well as making known class
distinction. This dual process leaves performance on the table as the function
is not specialized for either task. In this work, we introduce Cascading
Unknown Detection with Known Classification (Cas-DC), where we instead learn
specialized functions in a cascading fashion for both known/unknown detection
and fine class classification amongst the world of knowns. Our experiments and
analysis demonstrate that Cas-DC handily outperforms modern methods in open set
recognition when compared using AUROC scores and correct classification rate at
various true positive rates.

深度学习模型在封闭集假设下表现良好，但在开放集条件下部署时遇到困难。本文提出了一种级联未知检测与已知分类方法（Cas-DC），通过学习专门用于已知 / 未知检测和已知类别分类的级联函数，实现在开放集识别任务中优于现代方法的性能表现。

开集识别中基于已知分类的级联未知检测

Cascading Unknown Detection with Known Classification for Open Set  Recognition

Deep Learners (DLs) are the state-of-art predictive mechanism with
applications in many fields requiring complex high dimensional data processing.
Although conventional DLs get trained via gradient descent with
back-propagation, Kalman Filter (KF)-based techniques that do not need gradient
computation have been developed to approximate DLs. We propose a multi-arm
extension of a KF-based DL approximator that can mimic DL when the sample size
is too small to train a multi-arm DL. The proposed Matrix Ensemble Kalman
Filter-based multi-arm ANN (MEnKF-ANN) also performs explicit model stacking
that becomes relevant when the training sample has an unequal-size feature set.
Our proposed technique can approximate Long Short-term Memory (LSTM) Networks
and attach uncertainty to the predictions obtained from these LSTMs with
desirable coverage. We demonstrate how MEnKF-ANN can "adequately" approximate
an LSTM network trained to classify what carbohydrate substrates are digested
and utilized by a microbiome sample whose genomic sequences consist of
polysaccharide utilization loci (PULs) and their encoded genes.

我们提出了一种基于多臂扩展的卡尔曼滤波器的深度学习近似器，通过这种方法在样本量太小无法训练多臂深度学习模型时实现了对深度学习的近似。我们的技术可以逼近长短期记忆网络，并将从这些网络得到的预测结果附加不确定性，以用于对多糖利用基因组序列的微生物样本消化和利用何种碳水化合物底物的分类任务。