Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
143 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
46 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Self-Normalizing Neural Networks (1706.02515v5)

Published 8 Jun 2017 in cs.LG and stat.ML

Abstract: Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNNs) are rare. FNNs that perform well are typically shallow and, therefore cannot exploit many levels of abstract representations. We introduce self-normalizing neural networks (SNNs) to enable high-level abstract representations. While batch normalization requires explicit normalization, neuron activations of SNNs automatically converge towards zero mean and unit variance. The activation function of SNNs are "scaled exponential linear units" (SELUs), which induce self-normalizing properties. Using the Banach fixed-point theorem, we prove that activations close to zero mean and unit variance that are propagated through many network layers will converge towards zero mean and unit variance -- even under the presence of noise and perturbations. This convergence property of SNNs allows to (1) train deep networks with many layers, (2) employ strong regularization, and (3) to make learning highly robust. Furthermore, for activations not close to unit variance, we prove an upper and lower bound on the variance, thus, vanishing and exploding gradients are impossible. We compared SNNs on (a) 121 tasks from the UCI machine learning repository, on (b) drug discovery benchmarks, and on (c) astronomy tasks with standard FNNs and other machine learning methods such as random forests and support vector machines. SNNs significantly outperformed all competing FNN methods at 121 UCI tasks, outperformed all competing methods at the Tox21 dataset, and set a new record at an astronomy data set. The winning SNN architectures are often very deep. Implementations are available at: github.com/bioinf-jku/SNNs.

Citations (2,340)

Summary

  • The paper introduces a novel SELU activation that inherently induces self-normalization, enabling feed-forward networks to achieve stable deep learning.
  • The methodology leverages the Banach fixed-point theorem to guarantee neuron activations converge to zero mean and unit variance.
  • Experimental results across 121 UCI tasks and specialized benchmarks like Tox21 and HTRU2 validate superior performance over traditional architectures.

Self-Normalizing Neural Networks: An Overview

The paper "Self-Normalizing Neural Networks," presented by a research team from Johannes Kepler University Linz, introduces a novel class of neural networks termed Self-Normalizing Neural Networks (SNNs). This work addresses the challenges that standard feed-forward neural networks (FNNs) face in deep learning contexts and sets forth a methodologically grounded approach to enhance their performance and robustness.

Key Contributions

The authors offer a comprehensive solution to the problem of training deep FNNs by proposing a new activation function known as Scaled Exponential Linear Units (SELUs). This function inherently induces the property of self-normalization, whereby neuron activations are driven towards zero mean and unit variance. SNNs operate without explicit normalization techniques such as batch normalization, thereby addressing the perturbation and high variance issues often associated with the latter.

Theoretical Foundations

Central to the paper's contributions are the mathematical underpinnings that validate the stability and attracting nature of the proposed normalization. Utilizing the Banach fixed-point theorem, the authors demonstrate that the mean and variance of activations in SNNs naturally converge to a fixed point, ensuring stable training across deep network architectures. Moreover, they derive bounds on the variance propagation, ensuring that neither vanishing nor exploding gradients will occur. This property is particularly crucial for enabling the training of deep networks, typically a challenging task for FNNs.

Experimental Results

The experimental validation is thorough, encompassing a diverse range of tasks from the UCI machine learning repository, drug discovery benchmarks, and astronomy datasets. In these experiments, SNNs consistently outperform not only traditional FNNs but also other deep learning architectures such as residual and highway networks. Noteworthy is the experiment with the UCI repository, where SNNs achieved the best results across 121 tasks, indicating a broad applicability. Similarly, in specialized tasks like the Tox21 dataset for drug discovery and pulsar prediction in the HTRU2 dataset, SNNs set new benchmarks for performance.

Practical Implications

This work significantly shifts the paradigm for deploying deep FNNs across domains where deep learning has not been traditionally successful due to training instability or sensitivity to hyperparameters. The self-normalizing property alleviates the need for complex regularization and intricate normalization techniques, making SNNs a compelling choice for both industry applications and theoretical investigations.

Speculative Future Directions

By achieving stability in deep feed-forward architectures, this research opens several avenues for future exploration. One potential direction is extending SNN principles to other neural network structures, such as recurrent or convolutional networks, potentially leading to architectures that are agnostic to input variance. Additionally, the robust training offered by SNNs might inspire novel applications in areas requiring high-dimensional data representations or in networks necessitating intricate reasoning processes.

In conclusion, the introduction of Self-Normalizing Neural Networks represents a substantial contribution to neural network theory and practice, providing a methodology grounded in rigorous mathematics and validated by extensive empirical evidence. This work addresses fundamental limitations within FNNs, offering a path forward for their broader application in the domain of deep learning.

Github Logo Streamline Icon: https://streamlinehq.com
Youtube Logo Streamline Icon: https://streamlinehq.com