A new approach to the local and global explanation is proposed. It is based
on selecting a convex hull constructed for the finite number of points around
an explained instance. The convex hull allows us to consider a dual
representation of instances in the form of convex combinations of extreme
points of a produced polytope. Instead of perturbing new instances in the
Euclidean feature space, vectors of convex combination coefficients are
uniformly generated from the unit simplex, and they form a new dual dataset. A
dual linear surrogate model is trained on the dual dataset. The explanation
feature importance values are computed by means of simple matrix calculations.
The approach can be regarded as a modification of the well-known model LIME.
The dual representation inherently allows us to get the example-based
explanation. The neural additive model is also considered as a tool for
implementing the example-based explanation approach. Many numerical experiments
with real datasets are performed for studying the approach. The code of
proposed algorithms is available.

提出了一种基于选择围绕受解释实例的有限数据点构建的凸包的本地和全局解释新方法。该方法通过考虑由产生的多面体的极端点的凸组合形式的实例的双重表示来获得解释的特征重要性值。该方法被视为著名模型 LIME 的修改版本，具有例子为基础的解释方法实现的工具也被考虑。通过对真实数据集进行的许多数值实验来研究该方法，其中也提供了所提算法的代码。

基于双特征和示例的解释方法

Dual feature-based and example-based explanation methods

We propose KeypointGAN, a new method for recognizing the pose of objects from
a single image that for learning uses only unlabelled videos and a weak
empirical prior on the object poses. Video frames differ primarily in the pose
of the objects they contain, so our method distils the pose information by
analyzing the differences between frames. The distillation uses a new dual
representation of the geometry of objects as a set of 2D keypoints, and as a
pictorial representation, i.e. a skeleton image. This has three benefits: (1)
it provides a tight `geometric bottleneck' which disentangles pose from
appearance, (2) it can leverage powerful image-to-image translation networks to
map between photometry and geometry, and (3) it allows to incorporate empirical
pose priors in the learning process. The pose priors are obtained from unpaired
data, such as from a different dataset or modality such as mocap, such that no
annotated image is ever used in learning the pose recognition network. In
standard benchmarks for pose recognition for humans and faces, our method
achieves state-of-the-art performance among methods that do not require any
labelled images for training.

本文提出了 KeypointGAN 方法，通过从未标注的视频和基于弱领域先验知识的学习，仅使用单个图像就可以识别物体的姿态，利用一个新的物体的双重表示方法，并且这种方法可以在不使用标注图像的情况下获得最新的成果。

无监督学习视频中的可解释关键点

Self-supervised Learning of Interpretable Keypoints from Unlabelled  Videos

This paper presents a region-partition based attraction field dual
representation for line segment maps, and thus poses the problem of line
segment detection (LSD) as the region coloring problem. The latter is then
addressed by learning deep convolutional neural networks (ConvNets) for
accuracy, robustness and efficiency. For a 2D line segment map, our dual
representation consists of three components: (i) A region-partition map in
which every pixel is assigned to one and only one line segment; (ii) An
attraction field map in which every pixel in a partition region is encoded by
its 2D projection vector w.r.t. the associated line segment; and (iii) A
squeeze module which squashes the attraction field to a line segment map that
almost perfectly recovers the input one. By leveraging the duality, we learn
ConvNets to compute the attraction field maps for raw in-put images, followed
by the squeeze module for LSD, in an end-to-end manner. Our method rigorously
addresses several challenges in LSD such as local ambiguity and class
imbalance. Our method also harnesses the best practices developed in ConvNets
based semantic segmentation methods such as the encoder-decoder architecture
and the a-trous convolution. In experiments, our method is tested on the
WireFrame dataset and the YorkUrban dataset with state-of-the-art performance
obtained. Especially, we advance the performance by 4.5 percents on the
WireFrame dataset. Our method is also fast with 6.6~10.4 FPS, outperforming
most of existing line segment detectors.

本文提出基于区域划分的吸引场双重表示法来解决线段检测问题，并使用深度卷积神经网络进行学习，以提高准确性、鲁棒性和效率。该方法有效地解决线段检测中的各种挑战，并在 WireFrame 和 YorkUrban 数据集上取得了最先进的表现。