2 research outputs found
Learning A Deep Encoder for Hashing
We investigate the -constrained representation which
demonstrates robustness to quantization errors, utilizing the tool of deep
learning. Based on the Alternating Direction Method of Multipliers (ADMM), we
formulate the original convex minimization problem as a feed-forward neural
network, named \textit{Deep Encoder}, by introducing the novel
Bounded Linear Unit (BLU) neuron and modeling the Lagrange multipliers as
network biases. Such a structural prior acts as an effective network
regularization, and facilitates the model initialization. We then investigate
the effective use of the proposed model in the application of hashing, by
coupling the proposed encoders under a supervised pairwise loss, to develop a
\textit{Deep Siamese Network}, which can be optimized from end to
end. Extensive experiments demonstrate the impressive performances of the
proposed model. We also provide an in-depth analysis of its behaviors against
the competitors.Comment: To be presented at IJCAI'1
Learning A Task-Specific Deep Architecture For Clustering
While sparse coding-based clustering methods have shown to be successful,
their bottlenecks in both efficiency and scalability limit the practical usage.
In recent years, deep learning has been proved to be a highly effective,
efficient and scalable feature learning tool. In this paper, we propose to
emulate the sparse coding-based clustering pipeline in the context of deep
learning, leading to a carefully crafted deep model benefiting from both. A
feed-forward network structure, named TAGnet, is constructed based on a
graph-regularized sparse coding algorithm. It is then trained with
task-specific loss functions from end to end. We discover that connecting deep
learning to sparse coding benefits not only the model performance, but also its
initialization and interpretation. Moreover, by introducing auxiliary
clustering tasks to the intermediate feature hierarchy, we formulate DTAGnet
and obtain a further performance boost. Extensive experiments demonstrate that
the proposed model gains remarkable margins over several state-of-the-art
methods