Towards Regression-Free Neural Networks for Diverse Compute Platforms
This addresses the challenge of ensuring consistent AI behavior for on-device deep learning applications, though it is incremental as it builds on existing neural architecture search methods.
The paper tackles the problem of predictive inconsistencies (negative flips) in neural networks across diverse compute platforms by introducing REG-NAS, which reduces negative flips by 33-48% relative to the state-of-the-art while maintaining high accuracy.
With the shift towards on-device deep learning, ensuring a consistent behavior of an AI service across diverse compute platforms becomes tremendously important. Our work tackles the emergent problem of reducing predictive inconsistencies arising as negative flips: test samples that are correctly predicted by a less accurate model, but incorrectly by a more accurate one. We introduce REGression constrained Neural Architecture Search (REG-NAS) to design a family of highly accurate models that engender fewer negative flips. REG-NAS consists of two components: (1) A novel architecture constraint that enables a larger model to contain all the weights of the smaller one thus maximizing weight sharing. This idea stems from our observation that larger weight sharing among networks leads to similar sample-wise predictions and results in fewer negative flips; (2) A novel search reward that incorporates both Top-1 accuracy and negative flips in the architecture search metric. We demonstrate that \regnas can successfully find desirable architectures with few negative flips in three popular architecture search spaces. Compared to the existing state-of-the-art approach, REG-NAS enables 33-48% relative reduction of negative flips.