Peephole: Predicting Network Performance Before Training
This addresses the high cost of network design for deep learning practitioners, though it is incremental as it builds on existing encoding and prediction methods.
The paper tackles the problem of predicting neural network performance before training by encoding architectures with LSTM, achieving accurate predictions and consistent rankings across datasets.
The quest for performant networks has been a significant force that drives the advancements of deep learning in recent years. While rewarding, improving network design has never been an easy journey. The large design space combined with the tremendous cost required for network training poses a major obstacle to this endeavor. In this work, we propose a new approach to this problem, namely, predicting the performance of a network before training, based on its architecture. Specifically, we develop a unified way to encode individual layers into vectors and bring them together to form an integrated description via LSTM. Taking advantage of the recurrent network's strong expressive power, this method can reliably predict the performances of various network architectures. Our empirical studies showed that it not only achieved accurate predictions but also produced consistent rankings across datasets -- a key desideratum in performance prediction.