Comparing learned representations of deep neural networks

In recent years, a variety of deep neural network architectures have obtained substantial accuracy improvements in tasks such as image classification, speech recognition, and machine translation, yet little is known about how different neural networks learn. To further understand this, we interpret the function of a deep neural network used for classification as converting inputs to a hidden representation in a high dimensional space and applying a linear classifier in this space. This work focuses on comparing these representations as well as the learned input features for different state-of-the-art convolutional neural network architectures. By focusing on the geometry of this representation, we find that different network architectures trained on the same task have hidden representations which are related by linear transformations. We find that retraining the same network architecture with a different initialization does not necessarily lead to more similar representation geometry for most architectures, but the ResNeXt architecture consistently learns similar features and hidden representation geometry. We also study connections to adversarial examples and observe that networks with more similar hidden representation geometries also exhibit higher rates of adversarial example transferability.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2019

Author dc:creator

• Miglani, Vivek N.

Advisor dc:contributor.advisor

• Aleksander Ma̧dry.

• Electrical Engineering and Computer Science.

Statement dc:rights

• MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/123048

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/123048