论文标题
用于课堂学习的自适应聚合网络
Adaptive Aggregation Networks for Class-Incremental Learning
论文作者
论文摘要
班级学习（CIL）旨在学习一个分类模型，其中类逐渐增加的类别。 CIL中一个固有的问题是，新老式学习之间的稳定性 - 塑性困境，即高塑性模型很容易忘记旧类，但是高稳定性模型对于学习新课程却很弱。我们通过提出一种称为自适应聚合网络（AANETS）的新型网络架构来缓解这个问题，在该架构中，我们在每个残留级别（以Resnet作为基线体系结构）明确构建了两种类型的残留块：一个稳定的块和一个塑料块。我们从这两个块中汇总了输出特征图，然后将结果馈送到下一级块。我们适应聚合权重，以平衡这两种类型的块，即动态平衡稳定性和可塑性。我们对三个CIL基准测试：CIFAR-100，Imagenet-Subset和Imagenet进行了广泛的实验，并表明许多现有的CIL方法可以直接地纳入AANETS的体系结构中，以增强其性能。
Class-Incremental Learning (CIL) aims to learn a classification model with the number of classes increasing phase-by-phase. An inherent problem in CIL is the stability-plasticity dilemma between the learning of old and new classes, i.e., high-plasticity models easily forget old classes, but high-stability models are weak to learn new classes. We alleviate this issue by proposing a novel network architecture called Adaptive Aggregation Networks (AANets), in which we explicitly build two types of residual blocks at each residual level (taking ResNet as the baseline architecture): a stable block and a plastic block. We aggregate the output feature maps from these two blocks and then feed the results to the next-level blocks. We adapt the aggregation weights in order to balance these two types of blocks, i.e., to balance stability and plasticity, dynamically. We conduct extensive experiments on three CIL benchmarks: CIFAR-100, ImageNet-Subset, and ImageNet, and show that many existing CIL methods can be straightforwardly incorporated into the architecture of AANets to boost their performances.
