BIC

pytorch implementation of "Large Scale Incremental Learning" from https://arxiv.org/abs/1905.13260

Dataset

Download Cifar100 dataset from https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz

Put meta, train, test into ./cifar100

Train

python main.py

Result

	20	40	60	80	100
Paper	85.20	74.59	66.76	60.14	55.55
Implementation	83.00	69.90	63.08	57.11	53.70

Alpha & Beta

Adam (Bias correction layer)

	20	40	60	80	100
Alpha	1.0	0.788	0.718	0.700	0.696
Beta	0.0	-0.289	-0.310	-0.325	-0.327

SGD (Bias correction layer)

	20	40	60	80	100
Alpha	1.0	1.006	1.017	0.976	0.983
Beta	0.0	-2.809	-3.496	-3.447	-3.683

Different Optimizers make difference in alpha and beta.