TY  - CONF
T1  - Interpreting intentionally flawed models with linear probes
A1  - Graziani, Mara
A1  - Müller, Henning
A1  - Andrearczyk, Vincent
TI  - ICCV workshop on statistical deep learning in computer vision
Y1  - 2019
CY  - Seoul, Korea
KW  - Deep Learning
KW  - interpretability
KW  - wrong labels
N2  - The representational differences between generalizing networks and intentionally flawed models can be insightful on the dynamics of network training. Do memorizing networks, e.g. networks that learn random label correspondences, focus on specific patterns in the data to memorize the labels? Are the features learned by a generalizing network affected by randomization of the model parameters? In high-risk applications such as medical, legal or financial domains, highlighting the representational differences that help generalization may be even more important than model performance itself. In this paper, we probe the activations of intermediate layers with linear classification and regression. Results show that the bias towards simple solutions of generalizing networks is maintained even when statistical irregularities are intentionally introduced.
ER  -