Supervised Auto-Encoding Twin-Bottleneck Hashing
Yuan Chen Stéphane Marchand-Maillet
Deep hashing has shown to be a complexity-efficient solution for the
Approximate Nearest Neighbor search problem in high dimensional space. Many
methods usually build the loss function from pairwise or triplet data points to
capture the local similarity structure. Other existing methods construct the
similarity graph and consider all points simultaneously. Auto-encoding
Twin-bottleneck Hashing is one such method that dynamically builds the graph.
Specifically, each input data is encoded into a binary code and a continuous
variable, or the so-called twin bottlenecks. The similarity graph is then
computed from these binary codes, which get updated consistently during the
training. In this work, we generalize the original model into a supervised deep
hashing network by incorporating the label information. In addition, we examine
the differences of codes structure between these two networks and consider the
class imbalance problem especially in multi-labeled datasets. Experiments on
three datasets yield statistically significant improvement against the original
model. Results are also comparable and competitive to other supervised methods.