Humans have a remarkable ability to quickly and effectively learn new
concepts in a continuous manner without forgetting old knowledge. Though deep
learning has made tremendous successes on various computer vision tasks, it
faces challenges for achieving such human-level intelligence. In this paper, we
define a new problem called continual few-shot learning, in which tasks arrive
sequentially and each task is associated with a few training samples. We
propose Continual Meta-Learner (CML) to solve this problem. CML integrates
metric-based classification and a memory-based mechanism along with adversarial
learning into a meta-learning framework, which leads to the desirable
properties: 1) it can quickly and effectively learn to handle a new task; 2) it
overcomes catastrophic forgetting; 3) it is model-agnostic. We conduct
extensive experiments on two image datasets, MiniImageNet and CIFAR100.
Experimental results show that CML delivers state-of-the-art performance in
terms of classification accuracy on few-shot learning tasks without
catastrophic forgetting.

本文定义了一个新问题：不断出现的少样本学习；提出了 Continual Meta-Learner 来解决此问题，并在 MiniImageNet 和 CIFAR100 两个图像数据集上进行了广泛实验，表明 CML 在少量样本学习任务的分类准确性方面实现了现有最先进水平并避免了灾难性遗忘。

用对抗性类别存储进行连续少样本学习

Continual Few-Shot Learning with Adversarial Class Storage

Continually learning new classes from fresh data without forgetting previous
knowledge of old classes is a very challenging research problem. Moreover, it
is imperative that such learning must respect certain memory and computational
constraints such as (i) training samples are limited to only a few per class,
(ii) the computational cost of learning a novel class remains constant, and
(iii) the memory footprint of the model grows at most linearly with the number
of classes observed. To meet the above constraints, we propose C-FSCIL, which
is architecturally composed of a frozen meta-learned feature extractor, a
trainable fixed-size fully connected layer, and a rewritable dynamically
growing memory that stores as many vectors as the number of encountered
classes. C-FSCIL provides three update modes that offer a trade-off between
accuracy and compute-memory cost of learning novel classes. C-FSCIL exploits
hyperdimensional embedding that allows to continually express many more classes
than the fixed dimensions in the vector space, with minimal interference. The
quality of class vector representations is further improved by aligning them
quasi-orthogonally to each other by means of novel loss functions. Experiments
on the CIFAR100, miniImageNet, and Omniglot datasets show that C-FSCIL
outperforms the baselines with remarkable accuracy and compression. It also
scales up to the largest problem size ever tried in this few-shot setting by
learning 423 novel classes on top of 1200 base classes with less than 1.6%
accuracy drop. Our code is available at
this https URL

提出一种名为 C-FSCIL 的架构，利用超维嵌入，对每个新的类别进行异构存储策略，解决了记忆约束，并通过一些新的损失函数，进一步提高了类向量表示的质量。在 CIFAR100、miniImageNet 和 Omniglot 数据集上的实验结果表明，C-FSCIL 在几乎不用牺牲精度的情况下实现了很高的压缩比。