How Do Training Methods Influence the Utilization of Vision Models?
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
6716249236c0c14b5b1dc6a5
Paul Gavrikov
admin_assigned
2024-10-21T12:26:52.565Z
6266c07e7a1f5a1562c4113b
https://cdn-avatars.huggingface.co/v1/production/uploads/6266c07e7a1f5a1562c4113b/GEt70X7-UmNIbECDA0eVs.jpeg
Paul Gavrikov
user
paulgavrikov
6716249236c0c14b5b1dc6a6
Shashank Agnihotri
admin_assigned
2024-10-21T12:26:58.209Z
67162cabea37ca179f5965c9
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/7lKl5w8ecex4eP2I89MPz.png
Shashank Agnihotri
user
shashankskagnihotri
6716249236c0c14b5b1dc6a7
Margret Keuper
6716249236c0c14b5b1dc6a8
Janis Keuper
claimed_verified
2025-03-14T09:00:05.532Z
6519013e3fe8cc63b4c9b8d3
https://cdn-avatars.huggingface.co/v1/production/uploads/6519013e3fe8cc63b4c9b8d3/ShO-DM3nWhKTeg_qJ3p0P.jpeg
Janis Keuper
user
jkHSO
How Do Training Methods Influence the Utilization of Vision Models?
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
The training method significantly impacts the criticality of different layers in a neural network, with some training techniques emphasizing early layers and others deeper layers.
neural network learnable parameters weights decision function architecture task complexity training pipeline training method ImageNet-1k classification improved training regimes self-supervised training adversarial training layer criticality