Concretely, given an arbitrary image and a region of interest (e. g., eyes of face images), we manage to relate the latent space to the image region with the Jacobian matrix and then use low-rank factorization to discover steerable latent subspaces.
The experiments on cat and human-face data validate that our algorithm is able to learn the optimal generative models (e. g. ProGAN) with respect to specified quality metrics for noisy data.
In this paper, we show that the entanglement of the latent space for the VAE/GAN framework poses the main challenge for encoder learning.
Based on these sub-images, a local exposure for each sub-image is automatically learned by virtue of policy network sequentially while the reward of learning is globally designed for striking a balance of overall exposures.
To enhance the performance of LDSs, in this paper, we address the challenging issue of performing sparse coding on the space of LDSs, where both data and dictionary atoms are LDSs.
Representation learning has shown its effectiveness in many tasks such as image classification and text mining.
In the line search step, R3MC approximates the minimum point on the searching curve by minimizing on the line tangent to the curve.
The essence of distantly supervised relation extraction is that it is an incomplete multi-label classification problem with sparse and noisy features.
We explore the different roles of two fundamental concepts in graph theory, indegree and outdegree, in the context of clustering.
Ranked #1 on Image Clustering on Coil-20 (Accuracy metric)