Abstract
Deep convolutional neural networks (DCNN) with
manifold embedding have achieved considerable
attention in computer vision. However, prior arts
are usually based on the neighborhood-based graph
modeling only the pairwise relationship between
two samples, which fail to fully capture intra-class
variations and thus suffer from severe performance
loss for noisy data. While such intra-class variations can be well captured via sophisticated hypergraph structure, we are motivated and lead a
hypergraph induced Convolutional Manifold Network (H-CMN) to significantly improve the representation capacity of DCNN for the complex data.
Specifically, two innovative designs are provides:
1) our manifold preserving method is implemented
based on a mini-batch, which can be efficiently
plugged into the existing DCNN training pipelines
and be scalable for large datasets; 2) a robust hypergraph is built for each mini-batch, which not only
offers a strong robustness against typical noise, but
also captures the variances from multiple features.
Extensive experiments on the image classification
task on large benchmarking datasets demonstrate
that our model achieves much better performance
than the state-of-the-art.