iCaRL: Incremental Classifier and Representation Learning

A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data. In this work, we introduce a new training strategy, iCaRL, that allows learning in such a class-incremental way: only the training data for a small number of classes has to be present at the same time and new classes can be added progressively... iCaRL learns strong classifiers and a data representation simultaneously. This distinguishes it from earlier works that were fundamentally limited to fixed data representations and therefore incompatible with deep learning architectures. We show by experiments on CIFAR-100 and ImageNet ILSVRC 2012 data that iCaRL can learn many classes incrementally over a long period of time where other strategies quickly fail. read more

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Incremental Learning ImageNet-100 - 50 classes + 5 steps of 10 classes iCaRL Average Incremental Accuracy 65.56 # 3

Results from Other Papers


Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Incremental Learning CIFAR-100 - 50 classes + 10 steps of 5 classes iCaRL* Average Incremental Accuracy 52.57 # 8
Incremental Learning CIFAR-100 - 50 classes + 5 steps of 10 classes iCaRL* Average Incremental Accuracy 57.17 # 7
Incremental Learning ImageNet - 500 classes + 10 steps of 50 classes iCaRL* Average Incremental Accuracy 46.72 # 4

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