Recently, the fundamental problem of unsupervised domain adaptation (UDA) on
3D point clouds has been motivated by a wide variety of applications in
robotics, virtual reality, and scene understanding, to name a few. The point
cloud data acquisition procedures manifest themselves as significant domain
discrepancies and geometric variations among both similar and dissimilar
classes. The standard domain adaptation methods developed for images do not
directly translate to point cloud data because of their complex geometric
nature. To address this challenge, we leverage the idea of multimodality and
alignment between distributions. We propose a new UDA architecture for point
cloud classification that benefits from multimodal contrastive learning to get
better class separation in both domains individually. Further, the use of
optimal transport (OT) aims at learning source and target data distributions
jointly to reduce the cross-domain shift and provide a better alignment. We
conduct a comprehensive empirical study on PointDA-10 and GraspNetPC-10 and
show that our method achieves state-of-the-art performance on GraspNetPC-10
(with approx 4-12% margin) and best average performance on PointDA-10. Our
ablation studies and decision boundary analysis also validate the significance
of our contrastive learning module and OT alignment.

通过多模态对比学习和最优传输的对齐，我们提出了一种新的用于点云分类的无监督领域自适应架构，实现了更好的类别分离并在多个数据集上取得了最先进的性能。

将对比学习和最优输运融合于三维点云域适应

Synergizing Contrastive Learning and Optimal Transport for 3D Point  Cloud Domain Adaptation

Deep neural networks used for image classification often use convolutional
filters to extract distinguishing features before passing them to a linear
classifier. Most interpretability literature focuses on providing semantic
meaning to convolutional filters to explain a model's reasoning process and
confirm its use of relevant information from the input domain. Fully connected
layers can be studied by decomposing their weight matrices using a singular
value decomposition, in effect studying the correlations between the rows in
each matrix to discover the dynamics of the map. In this work we define a
singular value decomposition for the weight tensor of a convolutional layer,
which provides an analogous understanding of the correlations between filters,
exposing the dynamics of the convolutional map. We validate our definition
using recent results in random matrix theory. By applying the decomposition
across the linear layers of an image classification network we suggest a
framework against which interpretability methods might be applied using
hypergraphs to model class separation. Rather than looking to the activations
to explain the network, we use the singular vectors with the greatest
corresponding singular values for each linear layer to identify those features
most important to the network. We illustrate our approach with examples and
introduce the DeepDataProfiler library, the analysis tool used for this study.

本文提出一种基于张量的奇异值分解的方法，用于理解卷积层的动态过程和发现卷积特征之间的相关性，以及在图像分类网络中应用超图模型进行可解释性研究。

卷积权重的奇异值分解：一种 CNN 可解释性框架

The SVD of Convolutional Weights: A CNN Interpretability Framework

There are good arguments to support the claim that deep neural networks
(DNNs) capture better feature representations than the previous hand-crafted
feature engineering, which leads to a significant performance improvement. In
this paper, we move a tiny step towards understanding the dynamics of feature
representations over layers. Specifically, we model the process of class
separation of intermediate representations in pre-trained DNNs as the evolution
of communities in dynamic graphs. Then, we introduce modularity, a generic
metric in graph theory, to quantify the evolution of communities. In the
preliminary experiment, we find that modularity roughly tends to increase as
the layer goes deeper and the degradation and plateau arise when the model
complexity is great relative to the dataset. Through an asymptotic analysis, we
prove that modularity can be broadly used for different applications. For
example, modularity provides new insights to quantify the difference between
feature representations. More crucially, we demonstrate that the degradation
and plateau in modularity curves represent redundant layers in DNNs and can be
pruned with minimal impact on performance, which provides theoretical guidance
for layer pruning. Our code is available at
this https URL

本篇论文探讨深度神经网络的特征表示，并通过动态图的社区演化来量化并优化网络架构，探索神经网络架构的冗余层并提出层修剪方案。