Adaptive Labeling for Deep Learning to Hash
Explore this paper's citation graph
- Type
- article
- Published
- 2019-06-01
- Cited by
- 9
- References
- 37
- OpenAlex
- https://openalex.org/W2970251552
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:198904734
Keywords
Hash function, Feature hashing, Hamming space, Computer science, Universal hashing
References
- Supervised Discrete Hashing
- Deep learning of binary hash codes for fast image retrieval
- Deep semantic ranking based hashing for multi-label image retrieval
- Simultaneous feature learning and hash coding with deep neural networks
- Bit-Scalable Deep Hashing With Regularized Similarity Learning for Image Retrieval and Person Re-Identification
- Iterative Quantization: A Procrustean Approach to Learning Binary Codes for Large-Scale Image Retrieval
- Return of the Devil in the Details: Delving Deep into Convolutional Nets
- Semi-Supervised Hashing for Large-Scale Search
- Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
- ImageNet Large Scale Visual Recognition Challenge
- A new approach to interdomain routing based on secure multi-party computation
- Learning to Hash with Binary Reconstructive Embeddings
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
- Minimal Loss Hashing for Compact Binary Codes
- Supervised Hashing for Image Retrieval via Image Representation Learning
- Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks
- Image Style Transfer Using Convolutional Neural Networks
- SSDH: Semi-Supervised Deep Hashing for Large Scale Image Retrieval
- Deep Hashing Network for Efficient Similarity Retrieval
- Deep Quantization Network for Efficient Image Retrieval
Cited by
- Hadamard Codebook Based Deep Hashing
- Attention-aware invertible hashing network with skip connections
- SemanticHash: Hash Coding Via Semantics-Guided Label Prototype Learning
- PPIS-JOIN: A Novel Privacy-Preserving Image Similarity Join Method
- Learning Binary Hash Codes Based on Adaptable Label Representations
- Weighted Gaussian Loss based Hamming Hashing
- Contrastive Self-Supervised Learning as a Strong Baseline for Unsupervised Hashing
- Deep Learning to Hash With Application to Cross-View Nearest Neighbor Search
- Improving Deep Representation Learning via Auxiliary Learnable Target Coding
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