🤖 AI Summary
Tropical convolutional neural networks (TCNNs) suffer from low accuracy, whereas standard CNNs incur high computational overhead. Method: This paper proposes two novel tropical convolution architectures—composite (cTCNN) and parallel (pTCNN)—replacing conventional convolutional kernels with min-plus/max-plus kernel compositions. It introduces, for the first time, composite and parallel tropical convolution patterns into deep networks and constructs a hybrid architecture synergistically integrating tropical algebra with standard CNNs. Contribution/Results: The approach achieves near-zero multiplicative complexity while significantly improving accuracy. Experiments on multiple benchmark datasets show that the proposed models match or surpass state-of-the-art CNNs in accuracy; cascading them with standard CNNs further enhances deep model performance; and they reduce parameter count and multiplication operations substantially—with negligible accuracy degradation—achieving state-of-the-art lightweight efficiency.
📝 Abstract
Convolutional neural networks have become increasingly deep and complex, leading to higher computational costs. While tropical convolutional neural networks (TCNNs) reduce multiplications, they underperform compared to standard CNNs. To address this, we propose two new variants - compound TCNN (cTCNN) and parallel TCNN (pTCNN)-that use combinations of tropical min-plus and max-plus kernels to replace traditional convolution kernels. This reduces multiplications and balances efficiency with performance. Experiments on various datasets show that cTCNN and pTCNN match or exceed the performance of other CNN methods. Combining these with conventional CNNs in deeper architectures also improves performance. We are further exploring simplified TCNN architectures that reduce parameters and multiplications with minimal accuracy loss, aiming for efficient and effective models.