针对具体实例的模型扰动改进了广义零点学习。

IF 2.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guanyu Yang;Kaizhu Huang;Rui Zhang;Xi Yang
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引用次数: 0

摘要

零点学习(Zero-shot Learning,ZSL)指的是对训练过程中从未见过的新数据类别(未见类别)设计预测函数。在更实际的情况下,广义零点学习(GZSL)需要同时准确预测已见类和未见类。在没有目标样本的情况下,许多 GZSL 模型可能会过度拟合训练数据,并倾向于将个体预测为训练中出现过的类别。为了缓解这一问题,我们开发了一种参数化对抗训练过程,该过程可促进对已见类别的稳健识别,同时在测试过程中设计一种新颖的模型扰动机制,以确保对未见类别有足够的灵敏度。具体来说,对模型进行对抗扰动以获得特定实例的参数,从而在测试中对未见类别进行有偏差的预测。同时,鲁棒性训练可提高模型的鲁棒性,从而使预测结果几乎不受所见类别的影响。此外,通过同时计算多个个体的参数空间扰动,可以避免过于极端的扰动影响预测结果。在四个基准 ZSL 数据集上的比较结果表明,所提出的框架有效地改进了使用已学指标的零点方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning
Zero-shot learning (ZSL) refers to the design of predictive functions on new classes (unseen classes) of data that have never been seen during training. In a more practical scenario, generalized zero-shot learning (GZSL) requires predicting both seen and unseen classes accurately. In the absence of target samples, many GZSL models may overfit training data and are inclined to predict individuals as categories that have been seen in training. To alleviate this problem, we develop a parameter-wise adversarial training process that promotes robust recognition of seen classes while designing during the test a novel model perturbation mechanism to ensure sufficient sensitivity to unseen classes. Concretely, adversarial perturbation is conducted on the model to obtain instance-specific parameters so that predictions can be biased to unseen classes in the test. Meanwhile, the robust training encourages the model robustness, leading to nearly unaffected prediction for seen classes. Moreover, perturbations in the parameter space, computed from multiple individuals simultaneously, can be used to avoid the effect of perturbations that are too extreme and ruin the predictions. Comparison results on four benchmark ZSL data sets show the effective improvement that the proposed framework made on zero-shot methods with learned metrics.
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来源期刊
Neural Computation
Neural Computation 工程技术-计算机:人工智能
CiteScore
6.30
自引率
3.40%
发文量
83
审稿时长
3.0 months
期刊介绍: Neural Computation is uniquely positioned at the crossroads between neuroscience and TMCS and welcomes the submission of original papers from all areas of TMCS, including: Advanced experimental design; Analysis of chemical sensor data; Connectomic reconstructions; Analysis of multielectrode and optical recordings; Genetic data for cell identity; Analysis of behavioral data; Multiscale models; Analysis of molecular mechanisms; Neuroinformatics; Analysis of brain imaging data; Neuromorphic engineering; Principles of neural coding, computation, circuit dynamics, and plasticity; Theories of brain function.
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