Temporal Invariant Factor Disentangled Model for Representation Learning
| Autor: | Yunde Jia, Yuwei Wu, Weichao Shen |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Perspective (graphical) Representation (systemics) Pattern recognition 02 engineering and technology 010501 environmental sciences Object (computer science) 01 natural sciences Image (mathematics) Recurrent neural network 0202 electrical engineering electronic engineering information engineering Unsupervised learning 020201 artificial intelligence & image processing Artificial intelligence business Feature learning Categorical variable 0105 earth and related environmental sciences |
| Zdroj: | Pattern Recognition and Computer Vision ISBN: 9783030317225 PRCV (2) |
| Popis: | This paper focuses on disentangling different kinds of underlying explanatory factors from image sequences. From the temporal perspective, we divide the explanatory factors into the temporal-invariant factor and the temporal-variant factor. The temporal-invariant factor corresponds to the categorical concept of objects in an image sequence while the temporal-variant factor describes the object appearance changing. We propose a disentangled model to disentangle from an image sequence the temporal-invariant factor that is used as an object representation insensitive to appearance changes. Our model is built upon the variational auto-encoder (VAE) and the recurrent neural network (RNN) to independently approximate the posterior distributions of the factor in an unsupervised manner. Experimental results on the HeadPose image database show the effectiveness of the proposed method. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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