Table 1.

Summary of experimental results showing the final test accuracy (in percentages) for the RBP algorithms after 100 epochs of training on MNIST and CIFAR-10. For the experiments in this section, training was repeated five times with different weight initializations; in these cases the mean is provided, with the sample standard deviation in parentheses. Also included are the quantization results from Section 5, and the experiments applying dropout to the learning channel from Section 6.

	BP	RBP	SRBP	Top layer only
MNIST Baseline	97.9 (0.1)	97.2 (0.1)	97.2 (0.2)	84.7 (0.7)
No-f′	89.9 (0.3)	88.3 (1.1)	88.4 (0.7)
Adaptive		97.3 (0.1)	97.3 (0.1)
Sparse-8		96.0 (0.4)	96.9 (0.1)
Sparse-2		96.3 (0.5)	95.8 (0.2)
Sparse-1		90.3 (1.1)	94.6 (0.6)
Quantized error 5-bit	97.6	95.4	95.1
Quantized error 3-bit	96.5	92.5	93.2
Quantized error 1-bit	94.6	89.8	91.6
Quantized update 5-bit	95.2	94.0	93.3
Quantized update 3-bit	96.5	91.0	92.2
Quantized update 1-bit	92.5	9.6	90.7
LC Dropout 10%	97.7	96.5	97.1
LC Dropout 20%	97.8	96.7	97.2
LC Dropout 50%	97.7	96.7	97.1
CIFAR-10 Baseline	83.4 (0.2)	70.2 (1.1)	72.7 (0.8)	47.9 (0.4)
No-f′	54.8 (3.6)	32.7 (6.2)	39.9 (3.9)
Sparse-8		46.3 (4.3)	70.9 (0.7)
Sparse-2		62.9 (0.9)	65.7 (1.9)
Sparse-1		56.7 (2.6)	62.6 (1.8)