Recent progress towards learning from limited supervision has encouraged
efforts towards designing models that can recognize novel classes at test time
(generalized zero-shot learning or GZSL). GZSL approaches assume knowledge of
all classes, with or without labeled data, beforehand. However, practical
scenarios demand models that are adaptable and can handle dynamic addition of
new seen and unseen classes on the fly (that is continual generalized zero-shot
learning or CGZSL). One solution is to sequentially retrain and reuse
conventional GZSL methods, however, such an approach suffers from catastrophic
forgetting leading to suboptimal generalization performance. A few recent
efforts towards tackling CGZSL have been limited by difference in settings,
practicality, data splits and protocols followed-inhibiting fair comparison and
a clear direction forward. Motivated from these observations, in this work, we
firstly consolidate the different CGZSL setting variants and propose a new
Online-CGZSL setting which is more practical and flexible. Secondly, we
introduce a unified feature-generative framework for CGZSL that leverages
bi-directional incremental alignment to dynamically adapt to addition of new
classes, with or without labeled data, that arrive over time in any of these
CGZSL settings. Our comprehensive experiments and analysis on five benchmark
datasets and comparison with baselines show that our approach consistently
outperforms existing methods, especially on the more practical Online setting.

本研究提出了一种新的在线学习方法，旨在解决 continual generalized zero-shot learning 中动态添加新已知或未知类别所带来的挑战，该方法结合了双向增量对齐和特征生成框架以动态适应新类别的到来，并在五个基准数据集上表现出更好的泛化能力。

后来出现的未知类别？没问题

Unseen Classes at a Later Time? No Problem

Gaussian processes provide a flexible framework for forecasting, removing
noise, and interpreting long temporal datasets. State space modelling (Kalman
filtering) enables these non-parametric models to be deployed on long datasets
by reducing the complexity to linear in the number of data points. The
complexity is still cubic in the state dimension $m$ which is an impediment to
practical application. In certain special cases (Gaussian likelihood, regular
spacing) the GP posterior will reach a steady posterior state when the data are
very long. We leverage this and formulate an inference scheme for GPs with
general likelihoods, where inference is based on single-sweep EP (assumed
density filtering). The infinite-horizon model tackles the cubic cost in the
state dimensionality and reduces the cost in the state dimension $m$ to
$\mathcal{O}(m^2)$ per data point. The model is extended to online-learning of
hyperparameters. We show examples for large finite-length modelling problems,
and present how the method runs in real-time on a smartphone on a continuous
data stream updated at 100~Hz.

该文介绍了一种针对时间序列数据的非参数预测与降噪方法，使用高斯过程和卡尔曼滤波实现。此外还讲到了基于单次环境传输因子的推断方法，在算法复杂度方面有较好的优化表现，并且可以用于在线学习。