Transferability of features from multiple depths of a deep convolutional neural network
(2018) In Master's Theses in Mathematical Sciences FMAM05 20181Mathematics (Faculty of Engineering)
- Abstract
- Deep convolutional neural networks are great at learning structures in signals and sequential data. Their performance have surpassed most non-convolutional algorithms on classical problems within the field of image analysis. A reason behind their success is that even though these networks generally need a great amount of examples to learn from, they can be used to learn smaller tasks through different types of transfer learning techniques. When having small amounts of data, a common approach is to remove the output layer and use the remaining network as a feature extractor. In this work we attempt to quantify how network layers go from general to specific through extracting features from multiple depths. The transition was measured on some... (More)
- Deep convolutional neural networks are great at learning structures in signals and sequential data. Their performance have surpassed most non-convolutional algorithms on classical problems within the field of image analysis. A reason behind their success is that even though these networks generally need a great amount of examples to learn from, they can be used to learn smaller tasks through different types of transfer learning techniques. When having small amounts of data, a common approach is to remove the output layer and use the remaining network as a feature extractor. In this work we attempt to quantify how network layers go from general to specific through extracting features from multiple depths. The transition was measured on some different object classification problems by training classifiers both directly on the feature vectors and on combinations of the vectors.
The reached conclusion was that the feature from the very last layer of a deep convolutional network are very specific to the source task and using it to learn other classification problems is often sub-optimal. The best depth to extract features from depends on how similar the problem you want to learn is to the source task. (Less) - Popular Abstract (Swedish)
- Djupa faltningsnätverk har blivit otroligt populära de senaste åren tack vare sin förmåga att lära sig att förstå innehållet i signaler och bilder. Dessa djupa nätverk ligg\-er bakom moderna tekniker såsom självkörande bilar och avancerad ansiktsigenkänning i mobiltelefoner. Genom att undersöka hur strukturer kan utvinnas ur dessa nätverk så kan vi förbättra vår förmåga att lära oss nya problem.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/8943169
- author
- Widell, Emil LU and Edström, Jesper LU
- supervisor
-
- Karl Åström LU
- organization
- course
- FMAM05 20181
- year
- 2018
- type
- H2 - Master's Degree (Two Years)
- subject
- keywords
- Deep Learning, Deep Neural Networks, Transfer Learning, Feature Extraction
- publication/series
- Master's Theses in Mathematical Sciences
- report number
- LUTFMA-3347-2018
- ISSN
- 1404-6342
- other publication id
- 2018:E24
- language
- English
- id
- 8943169
- date added to LUP
- 2018-05-30 17:36:29
- date last changed
- 2018-05-30 17:36:29
@misc{8943169, abstract = {{Deep convolutional neural networks are great at learning structures in signals and sequential data. Their performance have surpassed most non-convolutional algorithms on classical problems within the field of image analysis. A reason behind their success is that even though these networks generally need a great amount of examples to learn from, they can be used to learn smaller tasks through different types of transfer learning techniques. When having small amounts of data, a common approach is to remove the output layer and use the remaining network as a feature extractor. In this work we attempt to quantify how network layers go from general to specific through extracting features from multiple depths. The transition was measured on some different object classification problems by training classifiers both directly on the feature vectors and on combinations of the vectors. The reached conclusion was that the feature from the very last layer of a deep convolutional network are very specific to the source task and using it to learn other classification problems is often sub-optimal. The best depth to extract features from depends on how similar the problem you want to learn is to the source task.}}, author = {{Widell, Emil and Edström, Jesper}}, issn = {{1404-6342}}, language = {{eng}}, note = {{Student Paper}}, series = {{Master's Theses in Mathematical Sciences}}, title = {{Transferability of features from multiple depths of a deep convolutional neural network}}, year = {{2018}}, }