Unsupervised Learning of Disentangled Representations from Video
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2017
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| _version_ | 1866913263392915456 |
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| author | Denton, Remi Birodkar, Vighnesh |
| author_facet | Denton, Remi Birodkar, Vighnesh |
| contents | We present a new model DrNET that learns disentangled image representations from video. Our approach leverages the temporal coherence of video and a novel adversarial loss to learn a representation that factorizes each frame into a stationary part and a temporally varying component. The disentangled representation can be used for a range of tasks. For example, applying a standard LSTM to the time-vary components enables prediction of future frames. We evaluate our approach on a range of synthetic and real videos, demonstrating the ability to coherently generate hundreds of steps into the future. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1705_10915 |
| institution | arXiv |
| publishDate | 2017 |
| record_format | arxiv |
| spellingShingle | Unsupervised Learning of Disentangled Representations from Video Denton, Remi Birodkar, Vighnesh Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition We present a new model DrNET that learns disentangled image representations from video. Our approach leverages the temporal coherence of video and a novel adversarial loss to learn a representation that factorizes each frame into a stationary part and a temporally varying component. The disentangled representation can be used for a range of tasks. For example, applying a standard LSTM to the time-vary components enables prediction of future frames. We evaluate our approach on a range of synthetic and real videos, demonstrating the ability to coherently generate hundreds of steps into the future. |
| title | Unsupervised Learning of Disentangled Representations from Video |
| topic | Machine Learning Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1705.10915 |