Compared to humans, machine learning models generally require significantly
more training examples and fail to extrapolate from experience to solve
previously unseen challenges. To help close this performance gap, we augment
single-task neural networks with a meta-recognition model which learns a
succinct model code via its autoencoder structure, using just a few informative
examples. The model code is then employed by a meta-generative model to
construct parameters for the task-specific model. We demonstrate that for
previously unseen tasks, without additional training, this Meta-Learning
Autoencoder (MeLA) framework can build models that closely match the true
underlying models, with loss significantly lower than given by fine-tuned
baseline networks, and performance that compares favorably with
state-of-the-art meta-learning algorithms. MeLA also adds the ability to
identify influential training examples and predict which additional data will
be most valuable to acquire to improve model prediction.

使用 meta-recognition 模型通过 autoencoder 方法，学习简洁的模型代码，并通过 meta-generative 模型构建任务特定模型参数，实现比 fine-tuned 基线网络更低的损失和与最先进的元学习算法相匹配的性能，同时还能够识别对模型预测有影响的训练样本和预测获取哪些附加数据将对改善模型预测最有价值。

基于元学习的自编码器进行少样本预测

Meta-learning autoencoders for few-shot prediction

Deep networks have produced significant gains for various visual recognition
problems, leading to high impact academic and commercial applications. Recent
work in deep networks highlighted that it is easy to generate images that
humans would never classify as a particular object class, yet networks classify
such images high confidence as that given class - deep network are easily
fooled with images humans do not consider meaningful. The closed set nature of
deep networks forces them to choose from one of the known classes leading to
such artifacts. Recognition in the real world is open set, i.e. the recognition
system should reject unknown/unseen classes at test time. We present a
methodology to adapt deep networks for open set recognition, by introducing a
new model layer, OpenMax, which estimates the probability of an input being
from an unknown class. A key element of estimating the unknown probability is
adapting Meta-Recognition concepts to the activation patterns in the
penultimate layer of the network. OpenMax allows rejection of "fooling" and
unrelated open set images presented to the system; OpenMax greatly reduces the
number of obvious errors made by a deep network. We prove that the OpenMax
concept provides bounded open space risk, thereby formally providing an open
set recognition solution. We evaluate the resulting open set deep networks
using pre-trained networks from the Caffe Model-zoo on ImageNet 2012 validation
data, and thousands of fooling and open set images. The proposed OpenMax model
significantly outperforms open set recognition accuracy of basic deep networks
as well as deep networks with thresholding of SoftMax probabilities.

该研究提出了一种新型的 OpenMax 模型，通过引入 Meta-Recognition 概念到网络的倒数第二层的激活模式中，估计输入图像来自未知类别的概率，从而实现深度学习神经网络的开放式识别，极大地提高了开放式识别准确性。