It is common to have continuous streams of new data that need to be
introduced in the system in real-world applications. The model needs to learn
newly added capabilities (future tasks) while retaining the old knowledge (past
tasks). Incremental learning has recently become increasingly appealing for
this problem. Task-incremental learning is a kind of incremental learning where
task identity of newly included task (a set of classes) remains known during
inference. A common goal of task-incremental methods is to design a network
that can operate on minimal size, maintaining decent performance. To manage the
stability-plasticity dilemma, different methods utilize replay memory of past
tasks, specialized hardware, regularization monitoring etc. However, these
methods are still less memory efficient in terms of architecture growth or
input data costs. In this study, we present a simple yet effective adjustment
network (SAN) for task incremental learning that achieves near state-of-the-art
performance while using minimal architectural size without using memory
instances compared to previous state-of-the-art approaches. We investigate this
approach on both 3D point cloud object (ModelNet40) and 2D image (CIFAR10,
CIFAR100, MiniImageNet, MNIST, PermutedMNIST, notMNIST, SVHN, and FashionMNIST)
recognition tasks and establish a strong baseline result for a fair comparison
with existing methods. On both 2D and 3D domains, we also observe that SAN is
primarily unaffected by different task orders in a task-incremental setting.

研究了一种基于简单有效的调整网络（SAN）的增量学习方法，达到了接近最先进的性能，使用最小的架构大小，而不需要使用先前最先进方法中的内存实例，对 3D 点云对象（ModelNet40）和 2D 图像（CIFAR10，CIFAR100，MiniImageNet，MNIST，PermutedMNIST，notMNIST，SVHN 和 FashionMNIST）识别任务进行了研究，并为公平比较现有方法建立了强有力的基准结果。在 2D 和 3D 领域，我们还观察到 SAN 在任务增量设置中基本不受不同任务顺序的影响。