Dynamic Semantic Channel Hashing Loss for Deep Semantic Hashing
A new loss function called Dynamic Semantic Channel Hashing (DSCH) is proposed for deep semantic hashing, addressing optimization challenges in existing methods. Semantic hashing generates short binary hash codes for efficient approximate nearest neighbor search in high-dimensional data. Deep learning approaches improve semantic capture over traditional manual feature engineering and enable cross-modal hash codes in a shared Hamming space. Previous work introduced semantic channels with fixed width and Hamming distances from label similarities, but this caused discontinuities in the loss landscape. DSCH dynamically adjusts channels to smooth optimization. The paper is published on arXiv with ID 2607.24567.
Key facts
- DSCH-Loss is a new loss function for deep semantic hashing.
- It addresses discontinuities in previous semantic channel loss functions.
- Semantic hashing enables efficient approximate nearest neighbor search.
- Deep learning methods offer better semantic capturing than traditional approaches.
- Cross-modal hash codes are generated in a shared Hamming space.
- Previous work used fixed-width semantic channels with label-derived distances.
- DSCH dynamically adjusts semantic channels to smooth optimization.
- The paper is available on arXiv with ID 2607.24567.
Entities
Institutions
- arXiv