Enhancing Robustness with Tilted Exponential Layer: A Communication-Theoretic Perspective

Core Concepts

The authors propose a novel approach, Tilted Exponential Layer, inspired by communication theory, to improve robustness in deep neural networks by enhancing the signal-to-noise ratio through neural competition. The core argument revolves around maximizing likelihood estimation of matched filters under a Gaussian noise model.

Abstract

The content introduces the Tilted Exponential Layer (TEXP) as a method to enhance robustness in deep neural networks by improving the signal-to-noise ratio through neural competition. By replacing traditional techniques like data augmentation, TEXP aims to provide better resilience against noise and other corruptions without compromising performance. Experimental results on standard image datasets demonstrate the effectiveness of TEXP in enhancing robustness across various distortions.
State-of-the-art techniques for improving robustness in deep networks rely heavily on empirical risk minimization and data augmentation. However, the proposed TEXP approach offers a complementary strategy inspired by communication theory.
TEXP learning involves maximizing likelihood estimation of matched filters under a Gaussian noise model to enhance signal-to-noise ratio.
Inference in a TEXP layer is achieved through a tilted softmax, providing posterior probabilities for competing signaling hypotheses represented by neurons.
Experiments on standard image datasets show that TEXP enhances robustness against noise and common corruptions without requiring data augmentation.
The study also explores combining TEXP with data augmentation techniques for cumulative gains in robustness against various distortions.

Stats

For example, adversarial training (Madry et al., 2018) augments training data with generated adversarial examples to improve defense against attacks.
The tilt parameter for clean training is set at t = 10/√D = 1.92 to make "harder" decisions favoring winners when learning signal templates.
In contrast, during inference, a higher value of ν2 (lower t) is used to accommodate data noise due to different distortions not present during training.

Quotes

"The proposed tilted exponential (TEXP) approach for robustness is based on fitting the model to the input x to a layer in a neural network."
"Our framework allows us to vary the amount of data noise during training and inference."

Key Insights Distilled From

Improving Robustness via Tilted Exponential Layer

by Bhagyashree ... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2311.01047.pdf

Deeper Inquiries

How does the concept of neuronal competition in TEXP relate to traditional methods like batch normalization

In the TEXP approach, neuronal competition plays a crucial role in enhancing robustness by promoting sparse, strong activations. This is achieved through the use of a tilted softmax and thresholding layer after convolution, where neurons compete to represent layer inputs sparsely. Neuronal competition in TEXP can be seen as an alternative to traditional methods like batch normalization. While batch normalization aims to normalize activations within each mini-batch during training to improve convergence and generalization, TEXP focuses on promoting competition among neurons to enhance signal-to-noise ratio and robustness against distortions.

What implications could the use of different tilt parameters during training and inference have on model performance

The use of different tilt parameters during training and inference in TEXP can have significant implications on model performance. During training, a smaller tilt parameter may be used when learning from clean data or controlled perturbations, allowing for harder decisions favoring winners when learning signal templates. On the other hand, during inference, a higher tilt parameter may be employed to accommodate data noise from various distortions not present during training. This flexibility in adjusting tilt parameters allows for adaptability based on the type of data being processed and can lead to improved robustness against noise and corruptions without compromising accuracy.

How might insights from communication theory influence future developments in deep learning research

Insights from communication theory offer valuable perspectives that could influence future developments in deep learning research. By drawing parallels between neural network operations and communication systems' signal processing techniques such as matched filtering and maximum likelihood estimation, researchers can explore novel approaches for enhancing neural network performance. These insights could inspire new methodologies for improving signal-to-noise ratio within neural networks, optimizing information transmission efficiency, and developing more robust models capable of handling diverse types of input data with greater resilience against disturbances or adversarial attacks.