We consider the problem of learning multiple tasks in a continual learning
setting in which data from different tasks is presented to the learner in a
streaming fashion. A key challenge in this setting is the so-called
"catastrophic forgetting problem", in which the performance of the learner in
an "old task" decreases when subsequently trained on a "new task". Existing
continual learning methods, such as Averaged Gradient Episodic Memory (A-GEM)
and Orthogonal Gradient Descent (OGD), address catastrophic forgetting by
minimizing the loss for the current task without increasing the loss for
previous tasks. However, these methods assume the learner knows when the task
changes, which is unrealistic in practice. In this paper, we alleviate the need
to provide the algorithm with information about task changes by using an online
clustering-based approach on a dynamically updated finite pool of samples or
gradients. We thereby successfully counteract catastrophic forgetting in one of
the hardest settings, namely: domain-incremental learning, a setting for which
the problem was previously unsolved. We showcase the benefits of our approach
by applying these ideas to projection-based methods, such as A-GEM and OGD,
which lead to task-agnostic versions of them. Experiments on real datasets
demonstrate the effectiveness of the proposed strategy and its promising
performance compared to state-of-the-art methods.

通过使用基于在线聚类的方法解决连续学习中的灾难性遗忘问题，成功地应用于域增量学习，并展示了这种方法在真实数据集上的有效性及与最先进方法的优越性能。

基于聚类的领域增量学习

Clustering-based Domain-Incremental Learning

We consider the fundamental problem of solving a large-scale system of linear
equations. In particular, we consider the setting where a taskmaster intends to
solve the system in a distributed/federated fashion with the help of a set of
machines, who each have a subset of the equations. Although there exist several
approaches for solving this problem, missing is a rigorous comparison between
the convergence rates of the projection-based methods and those of the
optimization-based ones. In this paper, we analyze and compare these two
classes of algorithms with a particular focus on the most efficient method from
each class, namely, the recently proposed Accelerated Projection-Based
Consensus (APC) and the Distributed Heavy-Ball Method (D-HBM). To this end, we
first propose a geometric notion of data heterogeneity called angular
heterogeneity and discuss its generality. Using this notion, we bound and
compare the convergence rates of the studied algorithms and capture the effects
of both cross-machine and local data heterogeneity on these quantities. Our
analysis results in a number of novel insights besides showing that APC is the
most efficient method in realistic scenarios where there is a large data
heterogeneity. Our numerical analyses validate our theoretical results.

比较基于投影和基于优化的方法在分布 / 联合求解大规模线性方程系统时的收敛速度，结果显示在现实情况下，加速投影协议是最有效的方法。

数据异构对分布式线性系统求解器收敛速率的影响

On the Effects of Data Heterogeneity on the Convergence Rates of  Distributed Linear System Solvers

Projecting the point cloud on the 2D spherical range image transforms the
LiDAR semantic segmentation to a 2D segmentation task on the range image.
However, the LiDAR range image is still naturally different from the regular 2D
RGB image; for example, each position on the range image encodes the unique
geometry information. In this paper, we propose a new projection-based LiDAR
semantic segmentation pipeline that consists of a novel network structure and
an efficient post-processing step. In our network structure, we design a FID
(fully interpolation decoding) module that directly upsamples the
multi-resolution feature maps using bilinear interpolation. Inspired by the 3D
distance interpolation used in PointNet++, we argue this FID module is a 2D
version distance interpolation on $(\theta, \phi)$ space. As a parameter-free
decoding module, the FID largely reduces the model complexity by maintaining
good performance. Besides the network structure, we empirically find that our
model predictions have clear boundaries between different semantic classes.
This makes us rethink whether the widely used K-nearest-neighbor
post-processing is still necessary for our pipeline. Then, we realize the
many-to-one mapping causes the blurring effect that some points are mapped into
the same pixel and share the same label. Therefore, we propose to process those
occluded points by assigning the nearest predicted label to them. This NLA
(nearest label assignment) post-processing step shows a better performance than
KNN with faster inference speed in the ablation study. On the SemanticKITTI
dataset, our pipeline achieves the best performance among all projection-based
methods with $64 \times 2048$ resolution and all point-wise solutions. With a
ResNet-34 as the backbone, both the training and testing of our model can be
finished on a single RTX 2080 Ti with 11G memory. The code is released.

本文中提出了基于投影的 LiDAR 语义分割 Pipeline，通过设计新颖的网络结构和高效的后处理步骤，在 SemanticKITTI 数据集上取得了最佳性能。