Master thesis defense Jens von der Heide

Master thesis defense Jens von der Heide

ដោយ Johannes Rabold នៅ
ចំនួនតប៖ 1

Dear all,

you are cordially invited to join the master thesis defense of Jens von der Heide taking place on the 22nd of February at 13:00 (s.t.). You can find the Zoom credentials below. The topic is as follows:

Exploring the Impact of Varying Class Distributions on Concept Embeddings Generated with Net2Vec

Since the victory of Deep Blue against the World Chess Champion Garry Kasparov

in 1997, the use of deep learning models has reached many domains of

human life. Yet, especially in sensitive areas like medical diagnoses, more comprehensibility

of the results is needed to increase the patient acceptance. The

discovery of more comprehensive deep learning classifiers is currently one of the

most active endeavors in the field of machine learning, and specifically in deep

learning. The framework Net2Vec seeks higher explainability by decoding semantic

concepts in neural network layers. The original authors used the entire

BRODEN dataset, a large image dataset densely labeled with concept segmentations

and classifications, in their experiments, without considering relations

between target classes. In order to extend this research, this thesis examines the

influence of different training data on the original Net2Vec results experimentally.

Particular semantic concept combinations of BRODEN are used as target

classes in various Net2Vec runs. The experiment results show the existence of

semantically related sub-concepts encoded in the convolutional layers. Greater

similarity of the target classes employed in the training lead to more expressive

and more unambiguous sub-concepts. These findings enable deep learning

model decisions with respect to semantic concepts to be more comprehensive.

ឆ្លើយតបទៅកាន់ Johannes Rabold

Re: Master thesis defense Jens von der Heide

ដោយ Johannes Rabold នៅ

Hey all,

a small reminder for the master thesis defense tomorrow held by Jens von der Heide. You can join at 13:00 via this link:

Zoom-Meeting beitreten

https://uni-bamberg.zoom.us/j/97551280681

 

Meeting-ID: 975 5128 0681

Kenncode: w=$E5d