Deep Unsupervised Hashing by Distilled Smooth Guidance
This addresses the challenge of efficient similarity search in domains with limited labeled data, representing an incremental improvement over existing unsupervised hashing techniques.
The paper tackled the problem of unsupervised deep hashing for approximate nearest neighbor search, which suffers from unreliable similarity signals, by proposing a method that learns distilled similarity and confidence signals, resulting in consistent outperformance over state-of-the-art methods on three benchmark datasets.
Hashing has been widely used in approximate nearest neighbor search for its storage and computational efficiency. Deep supervised hashing methods are not widely used because of the lack of labeled data, especially when the domain is transferred. Meanwhile, unsupervised deep hashing models can hardly achieve satisfactory performance due to the lack of reliable similarity signals. To tackle this problem, we propose a novel deep unsupervised hashing method, namely Distilled Smooth Guidance (DSG), which can learn a distilled dataset consisting of similarity signals as well as smooth confidence signals. To be specific, we obtain the similarity confidence weights based on the initial noisy similarity signals learned from local structures and construct a priority loss function for smooth similarity-preserving learning. Besides, global information based on clustering is utilized to distill the image pairs by removing contradictory similarity signals. Extensive experiments on three widely used benchmark datasets show that the proposed DSG consistently outperforms the state-of-the-art search methods.