Deriving discriminative classifiers from generative models

• URL: http://arxiv.org/abs/2201.00844v1
• Date: Mon, 3 Jan 2022 19:18:25 GMT
• Title: Deriving discriminative classifiers from generative models
• Authors: Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
• Abstract summary: We show how a generative classifier induced from a generative model can also be computed in a discriminative way from the same model. We illustrate the interest of the new discriminative way of computing classifiers in the Natural Language Processing (NLP) framework.
• Score: 6.939768185086753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We deal with Bayesian generative and discriminative classifiers. Given a model distribution $p(x, y)$, with the observation $y$ and the target $x$, one computes generative classifiers by firstly considering $p(x, y)$ and then using the Bayes rule to calculate $p(x | y)$. A discriminative model is directly given by $p(x | y)$, which is used to compute discriminative classifiers. However, recent works showed that the Bayesian Maximum Posterior classifier defined from the Naive Bayes (NB) or Hidden Markov Chain (HMC), both generative models, can also match the discriminative classifier definition. Thus, there are situations in which dividing classifiers into "generative" and "discriminative" is somewhat misleading. Indeed, such a distinction is rather related to the way of computing classifiers, not to the classifiers themselves. We present a general theoretical result specifying how a generative classifier induced from a generative model can also be computed in a discriminative way from the same model. Examples of NB and HMC are found again as particular cases, and we apply the general result to two original extensions of NB, and two extensions of HMC, one of which being original. Finally, we shortly illustrate the interest of the new discriminative way of computing classifiers in the Natural Language Processing (NLP) framework.

Related papers

• Generative Multi-modal Models are Good Class-Incremental Learners [51.5648732517187]
  We propose a novel generative multi-modal model (GMM) framework for class-incremental learning. Our approach directly generates labels for images using an adapted generative model. Under the Few-shot CIL setting, we have improved by at least 14% accuracy over all the current state-of-the-art methods with significantly less forgetting.
  arXiv  Detail & Related papers  (2024-03-27T09:21:07Z)
• Parametric Classification for Generalized Category Discovery: A Baseline Study [70.73212959385387]
  Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples. We investigate the failure of parametric classifiers, verify the effectiveness of previous design choices when high-quality supervision is available, and identify unreliable pseudo-labels as a key problem. We propose a simple yet effective parametric classification method that benefits from entropy regularisation, achieves state-of-the-art performance on multiple GCD benchmarks and shows strong robustness to unknown class numbers.
  arXiv  Detail & Related papers  (2022-11-21T18:47:11Z)
• GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models [74.0430727476634]
  We propose a new family of segmentation models that rely on a dense generative classifier for the joint distribution p(pixel feature,class) With a variety of segmentation architectures and backbones, GMMSeg outperforms the discriminative counterparts on closed-set datasets. GMMSeg even performs well on open-world datasets.
  arXiv  Detail & Related papers  (2022-10-05T05:20:49Z)
• Distance Based Image Classification: A solution to generative classification's conundrum? [70.43638559782597]
  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.
  arXiv  Detail & Related papers  (2022-10-04T03:35:13Z)
• A new class of generative classifiers based on staged tree models [2.66269503676104]
  Generative models for classification use the joint probability distribution of the class variable and the features to construct a decision rule. Here we introduce a new class of generative classifiers, called staged tree classifiers, which formally account for context-specific independence. An applied analysis to predict the fate of the passengers of the Titanic highlights the insights that the new class of generative classifiers can give.
  arXiv  Detail & Related papers  (2020-12-26T19:30:35Z)
• Using the Naive Bayes as a discriminative classifier [6.939768185086753]
  For classification tasks, probabilistic models can be categorized into two disjoint classes: generative or discriminative. The recent Entropic Forward-Backward algorithm shows that the Hidden Markov Model, considered as a generative model, can also match the discriminative one's definition. We show that the Naive Bayes classifier can also match the discriminative classifier definition, so it can be used in either a generative or a discriminative way.
  arXiv  Detail & Related papers  (2020-12-25T13:32:23Z)
• Learning and Evaluating Representations for Deep One-class Classification [59.095144932794646]
  We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks.
  arXiv  Detail & Related papers  (2020-11-04T23:33:41Z)
• Discriminatively-Tuned Generative Classifiers for Robust Natural Language Inference [59.62779187457773]
  We propose a generative classifier for natural language inference (NLI) We compare it to five baselines, including discriminative models and large-scale pretrained language representation models like BERT. Experiments show that GenNLI outperforms both discriminative and pretrained baselines across several challenging NLI experimental settings.
  arXiv  Detail & Related papers  (2020-10-08T04:44:00Z)
• Large scale analysis of generalization error in learning using margin based classification methods [2.436681150766912]
  We derive the expression for the generalization error of a family of large-margin classifiers in the limit of both sample size $n$ and dimension $p$. For two layer neural networks, we reproduce the recently developed double descent' phenomenology for several classification models.
  arXiv  Detail & Related papers  (2020-07-16T20:31:26Z)

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.