[Paper Review] Neural Stethoscopes: Unifying Analytic, Auxiliary and Adversarial Network Probing.

Fabian B. Fuchs, Oliver Groth+5 more|arXiv (Cornell University)|Jun 14, 2018

Anomaly Detection Techniques and Applications20 references5 citations

TL;DR

This paper introduces neural stethoscopes, a unified framework that quantifies and controls the influence of specific factors in deep neural networks using analytic, auxiliary, and adversarial probing. It demonstrates improved model robustness and performance in visual physics tasks by de-biasing predictions and enhancing relevant feature learning through targeted loss functions.

ABSTRACT

Model interpretability and systematic, targeted model adaptation present central tenets in machine learning for addressing limited or biased datasets. In this paper, we introduce neural stethoscopes as a framework for quantifying the degree of importance of specific factors of influence in deep networks as well as for actively promoting and suppressing information as appropriate. In doing so we unify concepts from multitask learning as well as training with auxiliary and adversarial losses. We showcase the efficacy of neural stethoscopes in an intuitive physicsdomain. Specifically, we investigate the challenge of visually predicting stability of block towers and demonstrate that the network uses visual cues which makes it susceptible to biases in the dataset. Through the use of stethoscopes we interrogate the accessibility of specific information throughout the network stack and show that we are able to actively de-bias network predictions as well as enhance performance via suitable auxiliary and adversarial stethoscope losses.

Motivation & Objective

• To address model bias and limited data in deep learning by enabling systematic interrogation of feature importance within neural networks.
• To unify concepts from multitask learning, auxiliary losses, and adversarial training into a single interpretability and adaptation framework.
• To improve model performance and robustness in visual reasoning tasks by actively promoting or suppressing specific information pathways.
• To provide a method for probing the accessibility of specific factors of influence throughout the network stack.

Proposed method

• Introducing neural stethoscopes as a framework to quantify the degree of influence of specific factors on network predictions using analytic probing.
• Employing auxiliary stethoscope heads to explicitly promote the learning of specific factors of interest during training.
• Applying adversarial stethoscope heads to suppress the influence of spurious or biased factors in the network's decision-making.
• Training the main network with a combination of standard, auxiliary, and adversarial stethoscope losses to balance information access and control.
• Using a shared encoder architecture where stethoscope heads are trained in parallel to monitor and modulate feature representations.
• Applying the framework in a visual physics domain—predicting block tower stability—using a dataset with potential biases in visual cues.

Experimental results

Research questions

• RQ1 To what extent can neural stethoscopes quantify the influence of specific factors (e.g., visual cues) on deep network predictions?
• RQ2 How effectively can auxiliary stethoscope losses enhance model performance by promoting relevant information?
• RQ3 Can adversarial stethoscope losses successfully suppress biased or spurious features in model predictions?
• RQ4 How does the integration of analytic, auxiliary, and adversarial probing improve model robustness and interpretability in a visual reasoning task?

Key findings

• Neural stethoscopes successfully identify that the network relies on visual cues such as block arrangement and color, which are prone to dataset bias.
• The use of auxiliary stethoscope losses improves prediction accuracy by enhancing the model’s access to relevant physical reasoning features.
• Adversarial stethoscope losses effectively reduce reliance on biased visual cues, leading to more robust and generalizable predictions.
• The unified framework enables systematic interrogation of feature accessibility across the network, revealing hidden biases and information bottlenecks.