Probing the Purview of Neural Networks via Gradient Analysis
- URL: http://arxiv.org/abs/2304.02834v2
- Date: Wed, 12 Apr 2023 06:17:23 GMT
- Title: Probing the Purview of Neural Networks via Gradient Analysis
- Authors: Jinsol Lee, Charlie Lehman, Mohit Prabhushankar, Ghassan AlRegib
- Abstract summary: We analyze the data-dependent capacity of neural networks and assess anomalies in inputs from the perspective of networks during inference.
To probe the purview of a network, we utilize gradients to measure the amount of change required for the model to characterize the given inputs more accurately.
We demonstrate that our gradient-based approach can effectively differentiate inputs that cannot be accurately represented with learned features.
- Score: 13.800680101300756
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We analyze the data-dependent capacity of neural networks and assess
anomalies in inputs from the perspective of networks during inference. The
notion of data-dependent capacity allows for analyzing the knowledge base of a
model populated by learned features from training data. We define purview as
the additional capacity necessary to characterize inference samples that differ
from the training data. To probe the purview of a network, we utilize gradients
to measure the amount of change required for the model to characterize the
given inputs more accurately. To eliminate the dependency on ground-truth
labels in generating gradients, we introduce confounding labels that are
formulated by combining multiple categorical labels. We demonstrate that our
gradient-based approach can effectively differentiate inputs that cannot be
accurately represented with learned features. We utilize our approach in
applications of detecting anomalous inputs, including out-of-distribution,
adversarial, and corrupted samples. Our approach requires no hyperparameter
tuning or additional data processing and outperforms state-of-the-art methods
by up to 2.7%, 19.8%, and 35.6% of AUROC scores, respectively.
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