Introspective Classification with Convolutional Nets

By Long Jin, Justin Lazarow, Zhuowen Tu. 2017

62 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

Citations in the corpus

Your Classifier is Secretly an Energy Based Model and You Should Treat it Like One (2019, background): “Also related are Introspective Networks (Jin et al., 2017; Lee et al., 2018) which have drawn a similar connection between discriminative classifiers and generative models.”
Flow Contrastive Estimation of Energy-Based Models (2019, methodology): “Generalizing from [69], [27, 40, 43] developed an introspective parameter estimation method, where the EBM is discriminatively learned and composed of a sequence of discriminative models obtained through the learning process.”
Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns (2019, methodology): “Our model in (2) directly corresponds to a classifier, specifically a spatial-temporal discriminative ConvNet, in the following sense [22], [51], [52], [53], [54], [55].”
Learning Descriptor Networks for 3D Shape Synthesis and Analysis (2018, background): “Building on the early work of [25], recently [8, 13] have developed an introspective learning method to learn the energy-based model, where the energy function is discriminatively learned.”
Learning Generative ConvNets via Multi-grid Modeling and Sampling (2017, background): “Model (1) corresponds to a classifier in the following sense [4, 45, 43, 22, 15].”
Dual Contradistinctive Generative Autoencoder (2020, neutral): “Energy-based generative models [56, 66, 31, 46] — which aim to directly model data density — are making a steady improvement for a simultaneously generative and discriminative single model.”
Learning Energy-Based Models by Diffusion Recovery Likelihood (2020, background): “EBMs [27, 33, 21, 47, 10, 43, 6, 7, 25, 35, 5, 11, 4, 8, 2, 12] are an appealing class of probabilistic models, which can be viewed as generative versions of discriminators [18, 26, 28, 12], yet can be learned from unlabeled data.”
Wasserstein Introspective Neural Networks (2017, methodology): “Although INN models [29, 21] point to a promising direction to obtain a single model being both a good generator and a strong discriminative classifier, a sequence of CNNs were needed to generate realistic synthesis.”
Cooperative Training of Descriptor and Generator Networks (2016, methodology): “Building on the pioneering work of [34], recently [35], [36], [37] have developed an introspective learning method to learn the energy-based model, where the energy function is discriminatively learned, and the learned energy function is used to generate synthesized examples via Langevin dynamics.”
Hybrid Discriminative-Generative Training via Contrastive Learning (2020, unknown): “Prior work also explicitly adopt discriminative models for generative modelling and show this iterative bootstrapping leads to improvement in classification, texture modeling, and other applications [50, 27, 21].”
Deep Co-Training for Semi-Supervised Image Recognition (2018, background): “They can serve as regularization techniques to smooth outputs [32], or create negative examples to tighten decision boundaries [23,33].”
Diffusion Models: A Comprehensive Survey of Methods and Applications (2022, background): “Energy-based Models (EBMs) [26, 48, 58, 64, 67, 68, 75, 78, 79, 120, 129, 132, 165, 170, 182, 196, 254, 281] can be viewed as one generative version of discriminators [79, 104, 131, 134], while can be learned from unlabeled input data.”
Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC (2020, background): “We may also learn the correction f θ ( x ) discriminatively, as in noise contrastive estimation (NCE) [19] or introspective neural networks (INN) [54, 25, 38].”