repository
Autoencoder for Stable Embedding
- Author(s)
- 김재현
- Alternative Author(s)
- Jaehyun Kim
- Advisor
- 신현정
- Department
- 일반대학원 인공지능학과
- Publisher
- The Graduate School, Ajou University
- Publication Year
- 2022-08
- Language
- kor
- Alternative Abstract
- Autoencoders are widely used machine learning models for dimensionality reduction and feature extraction tasks. However, these autoencoders have some limitations. The first is that the embedding space appearing in the model varies according to training even under the same conditions. The second is that the performance deviations also occur due to the variability of the embedding space. Third, the distance metric of the input data is lost in the embedding space, so the overall information of the data is lost, and the usability of the extracted features is hindered. This also means losing the manifold of the original data in the embedding space. Therefore, this study proposes a model that applies a new loss term with the difference between the pairwise similarity of the input data and the pairwise similarity of the bottleneck as a loss to solve the limitations of the autoencoder. This loss term is defined as a similarity loss. So, the proposed model is trained by adding the reconstruction loss used as a loss in a general autoencoder and a similarity loss as the total loss. As a result of the experiment, it was confirmed that the proposed model to which the similarity loss was applied showed up to about 49.58% lower embedding space variability than the general autoencoder and maintained the pairwise distance of the original data in the bottleneck. In addition, the deviation in performance was reduced by up to about 88.05%, and the reconstruction loss was reduced by about 23.79%. When the feature sets extracted from the proposed model and the general autoencoder were respectively applied to the classification model and the performance was compared, the case where the features extracted from the proposed model were applied showed about 15.31% higher classification performance. therefore, it is confirmed that the proposed model solves the variation in embedding space, performance deviation, and distance relationship loss of original data, which appears in existing autoencoders, and can increase performance and extract more useful features.
- Fulltext
- Appears in Collections:
- Graduate School of Ajou University > Department of Artificial Intelligence > 3. Theses(Master)
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