  Learning to hash has been widely applied to approximate nearest neighbor
search for large-scale multimedia retrieval, due to its computation efficiency
and retrieval quality. Deep learning to hash, which improves retrieval quality
by end-to-end representation learning and hash encoding, has received
increasing attention recently. Subject to the ill-posed gradient difficulty in
the optimization with sign activations, existing deep learning to hash methods
need to first learn continuous representations and then generate binary hash
codes in a separated binarization step, which suffer from substantial loss of
retrieval quality. This work presents HashNet, a novel deep architecture for
deep learning to hash by continuation method with convergence guarantees, which
learns exactly binary hash codes from imbalanced similarity data. The key idea
is to attack the ill-posed gradient problem in optimizing deep networks with
non-smooth binary activations by continuation method, in which we begin from
learning an easier network with smoothed activation function and let it evolve
during the training, until it eventually goes back to being the original,
difficult to optimize, deep network with the sign activation function.
Comprehensive empirical evidence shows that HashNet can generate exactly binary
hash codes and yield state-of-the-art multimedia retrieval performance on
standard benchmarks.
