Deep Learning to Ternary Hash Codes by Continuation

• URL: http://arxiv.org/abs/2107.07987v1
• Date: Fri, 16 Jul 2021 16:02:08 GMT
• Title: Deep Learning to Ternary Hash Codes by Continuation
• Authors: Mingrui Chen, Weiyu Li, Weizhi Lu
• Abstract summary: We propose to jointly learn the features with the codes by appending a smoothed function to the networks. Experiments show that the generated codes indeed could achieve higher retrieval accuracy.
• Score: 8.920717493647121
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, it has been observed that {0,1,-1}-ternary codes which are simply generated from deep features by hard thresholding, tend to outperform {-1,1}-binary codes in image retrieval. To obtain better ternary codes, we for the first time propose to jointly learn the features with the codes by appending a smoothed function to the networks. During training, the function could evolve into a non-smoothed ternary function by a continuation method. The method circumvents the difficulty of directly training discrete functions and reduces the quantization errors of ternary codes. Experiments show that the generated codes indeed could achieve higher retrieval accuracy.

Related papers

• SECRET: Towards Scalable and Efficient Code Retrieval via Segmented Deep Hashing [83.35231185111464]
  Deep learning has shifted the retrieval paradigm from lexical-based matching to encode source code and queries into vector representations. Previous research proposes deep hashing-based methods, which generate hash codes for queries and code snippets and use Hamming distance for rapid recall of code candidates. We propose a novel approach, which converts long hash codes calculated by existing deep hashing approaches into several short hash code segments through an iterative training strategy.
  arXiv  Detail & Related papers  (2024-12-16T12:51:35Z)
• Factor Graph Optimization of Error-Correcting Codes for Belief Propagation Decoding [62.25533750469467]
  Low-Density Parity-Check (LDPC) codes possess several advantages over other families of codes. The proposed approach is shown to outperform the decoding performance of existing popular codes by orders of magnitude.
  arXiv  Detail & Related papers  (2024-06-09T12:08:56Z)
• Learning Linear Block Error Correction Codes [62.25533750469467]
  We propose for the first time a unified encoder-decoder training of binary linear block codes. We also propose a novel Transformer model in which the self-attention masking is performed in a differentiable fashion for the efficient backpropagation of the code gradient.
  arXiv  Detail & Related papers  (2024-05-07T06:47:12Z)
• Interpreting Training Aspects of Deep-Learned Error-Correcting Codes [0.0]
  We look at developing tools for interpreting the training process for deep-learned error-correcting codes. All tools are demonstrated on TurboAE, but are applicable to other deep-learned forward error correcting codes.
  arXiv  Detail & Related papers  (2023-05-07T17:53:31Z)
• Soft-Labeled Contrastive Pre-training for Function-level Code Representation [127.71430696347174]
  We present textbfSCodeR, a textbfSoft-labeled contrastive pre-training framework with two positive sample construction methods. Considering the relevance between codes in a large-scale code corpus, the soft-labeled contrastive pre-training can obtain fine-grained soft-labels. SCodeR achieves new state-of-the-art performance on four code-related tasks over seven datasets.
  arXiv  Detail & Related papers  (2022-10-18T05:17:37Z)
• Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation [50.14232079160476]
  We propose a new approach with multimodal contrastive learning and soft data augmentation for code search. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages.
  arXiv  Detail & Related papers  (2022-04-07T08:49:27Z)
• Shuffle and Learn: Minimizing Mutual Information for Unsupervised Hashing [4.518427368603235]
  Unsupervised binary representation allows fast data retrieval without any annotations. Conflicts in binary space are one of the major barriers to high-performance unsupervised hashing. New relaxation method called Shuffle and Learn is proposed to tackle code conflicts in the unsupervised hash.
  arXiv  Detail & Related papers  (2020-11-20T07:14:55Z)
• Auto-Encoding Twin-Bottleneck Hashing [141.5378966676885]
  This paper proposes an efficient and adaptive code-driven graph. It is updated by decoding in the context of an auto-encoder. Experiments on benchmarked datasets clearly show the superiority of our framework over the state-of-the-art hashing methods.
  arXiv  Detail & Related papers  (2020-02-27T05:58:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.