Distance Based Image Classification: A solution to generative classification's conundrum?

• URL: http://arxiv.org/abs/2210.01349v1
• Date: Tue, 4 Oct 2022 03:35:13 GMT
• Title: Distance Based Image Classification: A solution to generative classification's conundrum?
• Authors: Wen-Yan Lin, Siying Liu, Bing Tian Dai, Hongdong Li
• Abstract summary: We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not. We propose a new generative model in which semantic factors are accommodated by shell theory's hierarchical generative process. We use the model to develop a classification scheme which suppresses the impact of noise while preserving semantic cues.
• Score: 70.43638559782597
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory's hierarchical generative process and non-semantic factors by an instance specific noise term. We use the model to develop a classification scheme which suppresses the impact of noise while preserving semantic cues. The result is a surprisingly accurate generative classifier, that takes the form of a modified nearest-neighbor algorithm; we term it distance classification. Unlike discriminative classifiers, a distance classifier: defines semantics by what-they-are; is amenable to incremental updates; and scales well with the number of classes.

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