Full
Outline
Interpretable visual image representation learning to reveal image variation factors is a hot research topic in computer vision. Many existing disentanglement methods discover variation factors of images and learn disentangled representations by using extra regularization term. However, it usually leads to an imbalance between disentanglement and generative quality, which affects visual image understanding. To address this issue, a generative visual image understanding method based on disentangled representation learning is proposed in terms of interpretable variations in images. Firstly, a pre-trained Glow model is designed to acquire the latent representations of target images. Secondly, a learning strategy based on image variation is constructed from the latent representations to obtain interpretable directions of candidate traversals. Finally, the contrast module is designed under the contrastive learning perspective to simulate image variations based on the interpretable directions of candidate traversals and then extract disentangled representations. The experimental results show that better results are achieved on the popular disentanglement datasets, which are Shapes3D, MPI3D, Anime, MNIST and Cars3D, where the MIG, DCI, FactorVAE score and β-VAE score metrics reach 0.16, 0.27, 0.89 and 0.98, respectively, on the Cars3D dataset, verifying the effectiveness and feasibility of the proposed method.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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