One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective

• URL: http://arxiv.org/abs/2109.14449v1
• Date: Wed, 29 Sep 2021 14:27:51 GMT
• Title: One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective
• Authors: Jiun Tian Hoe and Kam Woh Ng and Tianyu Zhang and Chee Seng Chan and Yi-Zhe Song and Tao Xiang
• Abstract summary: A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. We propose a novel deep hashing model with only a single learning objective. Our model is highly effective, outperforming the state-of-the-art multi-loss hashing models on three large-scale instance retrieval benchmarks.
• Score: 86.48094395282546
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A deep hashing model typically has two main learning objectives: to make the learned binary hash codes discriminative and to minimize a quantization error. With further constraints such as bit balance and code orthogonality, it is not uncommon for existing models to employ a large number (>4) of losses. This leads to difficulties in model training and subsequently impedes their effectiveness. In this work, we propose a novel deep hashing model with only a single learning objective. Specifically, we show that maximizing the cosine similarity between the continuous codes and their corresponding binary orthogonal codes can ensure both hash code discriminativeness and quantization error minimization. Further, with this learning objective, code balancing can be achieved by simply using a Batch Normalization (BN) layer and multi-label classification is also straightforward with label smoothing. The result is an one-loss deep hashing model that removes all the hassles of tuning the weights of various losses. Importantly, extensive experiments show that our model is highly effective, outperforming the state-of-the-art multi-loss hashing models on three large-scale instance retrieval benchmarks, often by significant margins. Code is available at https://github.com/kamwoh/orthohash

Related papers

• Efficient Syndrome Decoder for Heavy Hexagonal QECC via Machine Learning [1.1156329459915602]
  Recent advances have shown that topological codes can be efficiently decoded by deploying machine learning (ML) techniques. We first propose an ML based decoder for heavy hexagonal code and establish its efficiency in terms of the values of threshold and pseudo-threshold. A novel technique based on rank to determine the equivalent error classes is presented, which is empirically faster than the one based on linear search.
  arXiv  Detail & Related papers  (2022-10-18T10:16:14Z)
• A Lower Bound of Hash Codes' Performance [122.88252443695492]
  In this paper, we prove that inter-class distinctiveness and intra-class compactness among hash codes determine the lower bound of hash codes' performance. We then propose a surrogate model to fully exploit the above objective by estimating the posterior of hash codes and controlling it, which results in a low-bias optimization. By testing on a series of hash-models, we obtain performance improvements among all of them, with an up to $26.5%$ increase in mean Average Precision and an up to $20.5%$ increase in accuracy.
  arXiv  Detail & Related papers  (2022-10-12T03:30:56Z)
• One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching [19.831174790835732]
  Image hashing is a principled approximate nearest neighbor approach to find similar items to a query in a large collection of images. For optimal retrieval performance, producing balanced hash codes with low-quantization error is important. This paper considers an alternative approach to learning the quantization constraints. The task of learning balanced codes with low quantization error is re-formulated as matching the learned distribution of the continuous codes to a pre-defined discrete, uniform distribution.
  arXiv  Detail & Related papers  (2022-05-31T12:11:17Z)
• Deep Asymmetric Hashing with Dual Semantic Regression and Class Structure Quantization [9.539842235137376]
  We propose a dual semantic asymmetric hashing (DSAH) method, which generates discriminative hash codes under three-fold constrains. With these three main components, high-quality hash codes can be generated through network.
  arXiv  Detail & Related papers  (2021-10-24T16:14:36Z)
• CIMON: Towards High-quality Hash Codes [63.37321228830102]
  We propose a new method named textbfComprehensive stextbfImilarity textbfMining and ctextbfOnsistency leartextbfNing (CIMON) First, we use global refinement and similarity statistical distribution to obtain reliable and smooth guidance. Second, both semantic and contrastive consistency learning are introduced to derive both disturb-invariant and discriminative hash codes.
  arXiv  Detail & Related papers  (2020-10-15T14:47:14Z)
• ${\rm N{\small ode}S{\small ig}}$: Random Walk Diffusion meets Hashing for Scalable Graph Embeddings [7.025709586759654]
  $rm Nsmall odeSsmall ig$ is a scalable embedding model that computes binary node representations. $rm Nsmall odeSsmall ig$ exploits random walk diffusion probabilities via stable random projection hashing.
  arXiv  Detail & Related papers  (2020-10-01T09:07:37Z)
• Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing [1.8899300124593648]
  This paper investigates the robustness of hashing methods based on variational autoencoders to the lack of supervision. We propose a novel supervision method in which the model uses its label distribution predictions to implement the pairwise objective. Our experiments show that both methods can significantly increase the hash codes' quality.
  arXiv  Detail & Related papers  (2020-07-17T07:47:10Z)
• Learning to Hash with Graph Neural Networks for Recommender Systems [103.82479899868191]
  Graph representation learning has attracted much attention in supporting high quality candidate search at scale. Despite its effectiveness in learning embedding vectors for objects in the user-item interaction network, the computational costs to infer users' preferences in continuous embedding space are tremendous. We propose a simple yet effective discrete representation learning framework to jointly learn continuous and discrete codes.
  arXiv  Detail & Related papers  (2020-03-04T06:59:56Z)
• 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)
• AvgOut: A Simple Output-Probability Measure to Eliminate Dull Responses [97.50616524350123]
  We build dialogue models that are dynamically aware of what utterances or tokens are dull without any feature-engineering. The first model, MinAvgOut, directly maximizes the diversity score through the output distributions of each batch. The second model, Label Fine-Tuning (LFT), prepends to the source sequence a label continuously scaled by the diversity score to control the diversity level. The third model, RL, adopts Reinforcement Learning and treats the diversity score as a reward signal.
  arXiv  Detail & Related papers  (2020-01-15T18:32:06Z)

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.