AutoInit: Analytic Signal-Preserving Weight Initialization for Neural Networks

TL;DR

AutoInit is a versatile weight initialization method that analytically adapts to various neural network architectures, improving training stability and performance across diverse models and settings without data dependence.

Contribution

AutoInit introduces an automatic, analytically derived weight initialization scheme that adapts to different architectures and conditions, enhancing robustness and reliability.

Findings

Improves performance of CNNs, residual, and transformer networks.

More reliable than data-dependent methods across various settings.

Effective from small tasks to large datasets like ImageNet.

Abstract

Neural networks require careful weight initialization to prevent signals from exploding or vanishing. Existing initialization schemes solve this problem in specific cases by assuming that the network has a certain activation function or topology. It is difficult to derive such weight initialization strategies, and modern architectures therefore often use these same initialization schemes even though their assumptions do not hold. This paper introduces AutoInit, a weight initialization algorithm that automatically adapts to different neural network architectures. By analytically tracking the mean and variance of signals as they propagate through the network, AutoInit appropriately scales the weights at each layer to avoid exploding or vanishing signals. Experiments demonstrate that AutoInit improves performance of convolutional, residual, and transformer networks across a range of activation function, dropout, weight decay, learning rate, and normalizer settings, and does so more reliably than data-dependent initialization methods. This flexibility allows AutoInit to initialize models for everything from small tabular tasks to large datasets such as ImageNet. Such generality turns out particularly useful in neural architecture search and in activation function discovery. In these settings, AutoInit initializes each candidate appropriately, making performance evaluations more accurate. AutoInit thus serves as an automatic configuration tool that makes design of new neural network architectures more robust. The AutoInit package provides a wrapper around TensorFlow models and is available at https://github.com/cognizant-ai-labs/autoinit.