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增量学习
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 - 50 classes + 5 steps of 10 classes
PODNet (CNN)
CIFAR-100 - 50 classes + 10 steps of 5 classes
DER(Standard ResNet-18)
ImageNet100 - 10 steps
DER w/o Pruning
ImageNet - 10 steps
CIFAR-100-B0(5steps of 20 classes)
ImageNet-100 - 50 classes + 10 steps of 5 classes
RMM (ResNet-18)
ImageNet-100 - 50 classes + 5 steps of 10 classes
RMM (ResNet-18)
CIFAR100-B0(10steps of 10 classes)
CIFAR-100 - 50 classes + 25 steps of 2 classes
RMM (Modified ResNet-32)
CIFAR100B020Step(5ClassesPerStep)
ImageNet - 500 classes + 10 steps of 50 classes
PODNet
ImageNet - 500 classes + 5 steps of 100 classes
RMM (ResNet-18)
CIFAR-100 - 50 classes + 2 steps of 25 classes
TCIL
ImageNet-100 - 50 classes + 25 steps of 2 classes
CIFAR-100 - 50 classes + 50 steps of 1 class
PODNet
ImageNet-100 - 50 classes + 50 steps of 1 class
PODNet
CIFAR100B050S(2ClassesPerStep)
DER(ResNet-18)
CIFAR-100 - 40 classes + 60 steps of 1 class (Exemplar-free)
FeTrIL
ImageNet-10k - 5225 classes + 5 steps of 1045 classes
PPCA-CLIP
ImageNet - 500 classes + 25 steps of 20 classes
RMM (ResNet-18)
ImageNet100 - 20 steps
FOSTER
MLT17
MRM