LAB
Learnable Activation Binarizer for Binary Neural Networks
Sieger Falkena (Shell Global Solutions International B.V., Student TU Delft)
Hadi Jamali-Rad (TU Delft - Electrical Engineering, Mathematics and Computer Science, Shell Global Solutions International B.V.)
Jan van Gemert (TU Delft - Electrical Engineering, Mathematics and Computer Science)
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Abstract
Binary Neural Networks (BNNs) are receiving an up-surge of attention for bringing power-hungry deep learning towards edge devices. The traditional wisdom in this space is to employ sign(.) for binarizing feature maps. We argue and illustrate that sign(.) is a uniqueness bottleneck, limiting information propagation throughout the network. To alleviate this, we propose to dispense sign(.), replacing it with a learnable activation binarizer (LAB), allowing the network to learn a fine-grained binarization kernel per layer - as opposed to global thresholding. LAB is a novel universal module that can seamlessly be integrated into existing architectures. To confirm this, we plug it into four seminal BNNs and show a considerable accuracy boost at the cost of tolerable increase in delay and complexity. Finally, we build an end-to-end BNN (coined as LAB-BNN) around LAB, and demonstrate that it achieves competitive performance on par with the state-of-the-art on ImageNet. Our code can be found in our repository: https://github.com/sfalkena/LAB.