Transfer learning between domains: Evaluating the usefulness of transfer learning between object classification and audio classification
2020 (English)Independent thesis Basic level (degree of Bachelor), 20 credits / 30 HE credits
Student thesis
Abstract [en]
Convolutional neural networks have been successfully applied to both object classification and audio classification. The aim of this thesis is to evaluate the degree of how well transfer learning of convolutional neural networks, trained in the object classification domain on large datasets (such as CIFAR-10, and ImageNet), can be applied to the audio classification domain when only a small dataset is available. In this work, four different convolutional neural networks are tested with three configurations of transfer learning against a configuration without transfer learning. This allows for testing how transfer learning and the architectural complexity of the networks affects the performance. Two of the models developed by Google (Inception-V3, Inception-ResNet-V2), are used. These models are implemented using the Keras API where they are pre-trained on the ImageNet dataset. This paper also introduces two new architectures which are developed by the authors of this thesis. These are Mini-Inception, and Mini-Inception-ResNet, and are inspired by Inception-V3 and Inception-ResNet-V2, but with a significantly lower complexity. The audio classification dataset consists of audio from RC-boats which are transformed into mel-spectrogram images. For transfer learning to be possible, Mini-Inception, and Mini-Inception-ResNet are pre-trained on the dataset CIFAR-10. The results show that transfer learning is not able to increase the performance. However, transfer learning does in some cases enable models to obtain higher performance in the earlier stages of training.
Place, publisher, year, edition, pages
2020. , p. 90
Keywords [en]
Convolutional neural networks, Object classification, Audio classification, Transfer learning, Inception-V3, Inception-ResNet-V2, Keras, ImageNet, Mini-Inception, Mini-Inception-ResNet, Mel-spectrogram, CIFAR-10
National Category
Information Systems, Social aspects
Identifiers
URN: urn:nbn:se:his:diva-18669OAI: oai:DiVA.org:his-18669DiVA, id: diva2:1448160
External cooperation
Combitech
Subject / course
Informationsteknologi
Educational program
Computer Science - Specialization in Systems Development
Supervisors
Examiners
2020-06-262020-06-262020-06-26Bibliographically approved