Introspective Neural Networks for Generative Modeling

By Justin Lazarow, Long Jin, Zhuowen Tu. 2017

68 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Computer Vision and Pattern Recognition, Generative Models

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Citations in the corpus

Flow Contrastive Estimation of Energy-Based Models (2019, methodology): “Generalizing from [69], [27, 40, 43] developed an intro-spective 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].”
On the Anatomy of MCMC-based Maximum Likelihood Learning of Energy-Based Models (2019, background): “The works (Jin, Lazarow, and Tu 2017; Lazarow, Jin, and Tu 2017; Lee et al. 2018) learn a ConvNet potential in a discriminative framework.”
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 discrimi-natively learned.”
Learning Generative ConvNets via Multi-grid Modeling and Sampling (2017, background): “Recently, building on the early work of [43], [15, 22] have developed an introspective learning method to learn the energy-based model, where the energy function is discrim-inatively learned.”
Large Scale Image Completion via Co-Modulated Generative Adversarial Networks (2021, background): “The unpaired setting is also investigated when paired training data is not available (Choi et al., 2018; Huang et al., 2018; Kim et al., 2019; Lazarow et al., 2017; Liu et al., 2017; Yi et al., 2017; Zhao et al., 2020b; Zhu et al., 2017a).”
Dual Contradistinctive Generative Autoencoder (2020, neutral): “Energy-based generative models [57, 67, 43, 32, 47] — 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.”
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.”
Introspective Classification with Convolutional Nets (2017, background): “A recent work proposed in [21] is in the same family of ICN, but [21] focuses on unsupervised image modeling using a cascade of CNNs.”
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].”
Learning Energy-based Model with Flow-based Backbone by Neural Transport MCMC (2020, methodology): “This idea has been explored in noise contrastive estimation (NCE) [19, 14] and introspective neural networks (INN) [54, 25, 38], where the correction is obtained by discriminative learning.”
Deep Set Prediction Networks (2019, methodology): “Our inspiration for using backpropagation through an encoder as a decoder comes from the line of introspective neural networks [9; 10] for image modeling.”
On Learning Non-Convergent Short-Run MCMC Toward Energy-Based Model (2019, methodology): “Generalizing [45], [20, 26, 30] developed an introspective learning method where the EBM is discriminatively learned, and the EBM is both a generative model and a discriminative model.”