Incremental Learning
增量学习旨在开发能够从新数据中持续学习以应对新任务的人工智能系统，同时保留已从先前任务中习得的知识。该方法通过不断更新模型，使其在不遗忘旧知识的前提下适应新环境，从而提高系统的长期适应性和效率，具有重要的应用价值。
CIFAR-100 - 40 classes + 60 steps of 1 class (Exemplar-free)
FeTrIL
CIFAR-100 - 50 classes + 10 steps of 5 classes
DER(Standard ResNet-18)
CIFAR-100 - 50 classes + 2 steps of 25 classes
TCIL
CIFAR-100 - 50 classes + 25 steps of 2 classes
RMM (Modified ResNet-32)
CIFAR-100 - 50 classes + 5 steps of 10 classes
PPCA-SWSL
CIFAR-100 - 50 classes + 50 steps of 1 class
PODNet
CIFAR-100-B0(5steps of 20 classes)
CIFAR100-B0(10steps of 10 classes)
CIFAR100B020Step(5ClassesPerStep)
CIFAR100B050S(2ClassesPerStep)
DER(ResNet-18)
ImageNet - 10 steps
ImageNet-100 - 50 classes + 10 steps of 5 classes
RMM (ResNet-18)
ImageNet-100 - 50 classes + 25 steps of 2 classes
ImageNet-100 - 50 classes + 5 steps of 10 classes
RMM (ResNet-18)
ImageNet-100 - 50 classes + 50 steps of 1 class
PODNet
ImageNet-10k - 5225 classes + 5 steps of 1045 classes
PPCA-CLIP
ImageNet - 500 classes + 10 steps of 50 classes
PODNet
ImageNet - 500 classes + 25 steps of 20 classes
RMM (ResNet-18)
ImageNet - 500 classes + 5 steps of 100 classes
RMM (ResNet-18)
ImageNet100 - 10 steps
RMM (ResNet-18)
ImageNet100 - 20 steps
FOSTER
MLT17
MRM