The aim of few-shot learning (FSL) is to learn how to recognize image
categories from a small number of training examples. A central challenge is
that the available training examples are normally insufficient to determine
which visual features are most characteristic of the considered categories. To
address this challenge, we organize these visual features into facets, which
intuitively group features of the same kind (e.g. features that are relevant to
shape, color, or texture). This is motivated from the assumption that (i) the
importance of each facet differs from category to category and (ii) it is
possible to predict facet importance from a pre-trained embedding of the
category names. In particular, we propose an adaptive similarity measure,
relying on predicted facet importance weights for a given set of categories.
This measure can be used in combination with a wide array of existing
metric-based methods. Experiments on miniImageNet and CUB show that our
approach improves the state-of-the-art in metric-based FSL.

本文提出了一种基于 facets 的自适应相似性度量方法，该方法可以被用于改进现有的基于度量的 few-shot learning 模型，并在 miniImageNet 和 CUB 数据集上实现了 state-of-the-art 的表现。

多方位原型的少样本图像分类

Few-shot Image Classification with Multi-Facet Prototypes

Deep convolutional neural networks (CNNs) have achieved breakthrough
performance in many pattern recognition tasks such as image classification.
However, the development of high-quality deep models typically relies on a
substantial amount of trial-and-error, as there is still no clear understanding
of when and why a deep model works. In this paper, we present a visual
analytics approach for better understanding, diagnosing, and refining deep
CNNs. We formulate a deep CNN as a directed acyclic graph. Based on this
formulation, a hybrid visualization is developed to disclose the multiple
facets of each neuron and the interactions between them. In particular, we
introduce a hierarchical rectangle packing algorithm and a matrix reordering
algorithm to show the derived features of a neuron cluster. We also propose a
biclustering-based edge bundling method to reduce visual clutter caused by a
large number of connections between neurons. We evaluated our method on a set
of CNNs and the results are generally favorable.

本论文提出了一种基于可视化分析的方法，以更好地理解、诊断和改进深度卷积神经网络，并使用一些新的算法和技术来显示神经元和神经元群之间的多个方面和交互作用。

深度卷积神经网络分析的改进

Towards Better Analysis of Deep Convolutional Neural Networks

As the text databases available to users become larger and more
heterogeneous, genre becomes increasingly important for computational
linguistics as a complement to topical and structural principles of
classification. We propose a theory of genres as bundles of facets, which
correlate with various surface cues, and argue that genre detection based on
surface cues is as successful as detection based on deeper structural
properties.

提出了一种流派理论作为束的多个方面，这些方面与各种表面线索相关，认为基于表面线索的流派检测与基于深层结构属性的检测一样成功。