How Do Training Methods Influence the Utilization of Vision Models?
Authors: name: Paul Gavrikov, name: Shashank Agnihotri, name: Margret Keuper, name: Janis Keuper

Not all learnable parameters (e.g., weights) contribute equally to a neural
network's decision function. In fact, entire layers' parameters can sometimes
be reset to random values with little to no impact on the model's decisions. We
revisit earlier studies that examined how architecture and task complexity
influence this phenomenon and ask: is this phenomenon also affected by how we
train the model? We conducted experimental evaluations on a diverse set of
ImageNet-1k classification models to explore this, keeping the architecture and
training data constant but varying the training pipeline. Our findings reveal
that the training method strongly influences which layers become critical to
the decision function for a given task. For example, improved training regimes
and self-supervised training increase the importance of early layers while
significantly under-utilizing deeper layers. In contrast, methods such as
adversarial training display an opposite trend. Our preliminary results extend
previous findings, offering a more nuanced understanding of the inner mechanics
of neural networks.
  Code: https://github.com/paulgavrikov/layer_criticality