  Class-Incremental Learning (CIL) aims to solve the neural networks'
catastrophic forgetting problem, which refers to the fact that once the network
updates on a new task, its performance on previously-learned tasks drops
dramatically. Most successful CIL methods incrementally train a feature
extractor with the aid of stored exemplars, or estimate the feature
distribution with the stored prototypes. However, the stored exemplars would
violate the data privacy concerns, while the stored prototypes might not
reasonably be consistent with a proper feature distribution, hindering the
exploration of real-world CIL applications. In this paper, we propose a method
of \textit{e}mbedding distillation and \textit{Ta}sk-oriented
\textit{g}eneration (\textit{eTag}) for CIL, which requires neither the
exemplar nor the prototype. Instead, eTag achieves a data-free manner to train
the neural networks incrementally. To prevent the feature extractor from
forgetting, eTag distills the embeddings of the network's intermediate blocks.
Additionally, eTag enables a generative network to produce suitable features,
fitting the needs of the top incremental classifier. Experimental results
confirmed that our proposed eTag considerably outperforms the state-of-the-art
methods on CIFAR-100 and ImageNet-sub\footnote{Our code is available in the
Supplementary Materials.
