Deephash Getting Regularization Depth And Fine-tuning Right
Lin Jie, Morere Olivier, Chandrasekhar Vijay, Veillard Antoine, Goh Hanlin. Arxiv 2015
This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to making DeepHash work at extremely low bitrates are three important considerations – regularization, depth and fine-tuning – each requiring solutions specific to the hashing problem. In-depth evaluation shows that our scheme consistently outperforms state-of-the-art methods across all data sets for both Fisher Vectors and Deep Convolutional Neural Network features, by up to 20 percent over other schemes. The retrieval performance with 256-bit hashes is close to that of the uncompressed floating point features – a remarkable 512 times compression.
Similar Work