Title: MABA-Net: Masked Additive Binary Activation Network. Abstract: Despite significant reduction in memory footprint and computational cost, binary neural networks suffer from noticeable accuracy degradation compared to real-valued counterparts. A few works have attempted to narrow the accuracy gap by increasing the representation bit-width or the network width/depth, but they come at the expense of increased memory and/or compute. In this work, we find that the imbalanced ratio of activations to weights may be the main cause of degraded performance and increased memory overhead. We propose Masked Additive Binary Activation Network (MABA-Net) to reduce approximation errors and the activation bit-width, with minimum increase in the activation size. MABA-Net balances the ratio of the activation size to the weight size, leading to significant memory saving on large CNNs. We demonstrate MABA-Net's superior performance on the ImageNet dataset under various network configurations. Experimental results show that MABA-Net achieves competitive accuracy without increase of computational cost, while reducing memory usage compared to state-of-the-art. We will release the codes upon acceptance.