1,204 research outputs found
On Trivial Solution and High Correlation Problems in Deep Supervised Hashing
Deep supervised hashing (DSH), which combines binary learning and convolutional neural network, has attracted considerable research interests and achieved promising performance for highly efficient image retrieval. In this paper, we show that the widely used loss functions, pair-wise loss and triplet loss, suffer from the trivial solution problem and usually lead to highly correlated bits in practice, limiting the performance of DSH. One important reason is that it is difficult to incorporate proper constraints into the loss functions under the mini-batch based optimization algorithm. To tackle these problems, we propose to adopt ensemble learning strategy for deep model training. We found out that this simple strategy is capable of effectively decorrelating different bits, making the hashcodes more informative. Moreover, it is very easy to parallelize the training and support incremental model learning, which are very useful for real-world applications but usually ignored by existing DSH approaches. Experiments on benchmarks demonstrate the proposed ensemble based DSH can improve the performance of DSH approaches significant
Hashing as Tie-Aware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in nearest
neighbor retrieval. In this paper, we develop learning to rank formulations for
hashing, aimed at directly optimizing ranking-based evaluation metrics such as
Average Precision (AP) and Normalized Discounted Cumulative Gain (NDCG). We
first observe that the integer-valued Hamming distance often leads to tied
rankings, and propose to use tie-aware versions of AP and NDCG to evaluate
hashing for retrieval. Then, to optimize tie-aware ranking metrics, we derive
their continuous relaxations, and perform gradient-based optimization with deep
neural networks. Our results establish the new state-of-the-art for image
retrieval by Hamming ranking in common benchmarks.Comment: 15 pages, 3 figures. IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 201
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