Encouraging Disentangled and Convex Representation with Controllable Interpolation Regularization

no code implementations • 6 Dec 2021 • Yunhao Ge, Zhi Xu, Yao Xiao, Gan Xin, Yunkui Pang, Laurent Itti

(2) They lack convexity constraints, which is important for meaningfully manipulating specific attributes for downstream tasks.

Data Augmentation Disentanglement +2

Generative Auto-Encoder: Non-adversarial Controllable Synthesis with Disentangled Exploration

no code implementations • 1 Jan 2021 • Yunhao Ge, Gan Xin, Zhi Xu, Yao Xiao, Yunkui Pang, Yining HE, Laurent Itti

DEAE can become a generative model and synthesis semantic controllable samples by interpolating latent code, which can even synthesis novel attribute value never is shown in the original dataset.

Attribute Data Augmentation +2

Zero-shot Synthesis with Group-Supervised Learning

1 code implementation • ICLR 2021 • Yunhao Ge, Sami Abu-El-Haija, Gan Xin, Laurent Itti

Visual cognition of primates is superior to that of artificial neural networks in its ability to 'envision' a visual object, even a newly-introduced one, in different attributes including pose, position, color, texture, etc.