Few-shot Generative Modelling with Generative Matching Networks

By Sergey Bartunov, Dmitry P. Vetrov. 2018

105 citations recorded by the source corpus.

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In Generative Models

Citations in the corpus

D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation (2021, background): “In many cases, we do not have direct access to the true f(x), so we need to learn an accurate model from labeled data [6] (e.”
Meta-Transfer Learning for Few-Shot Learning (2018, critical): “underperform when trained on few-shot data [1].”
Generalizing from a Few Examples (2019, methodology): “HB [97] a hierarchy of θ as one of Dtrain c ’s HDP-DBM [116] a hierarchy of θ as one of Dtrain c ’s Latent Variable Neural Statistician [29] θ as input VERSA [40] θ as input SeqGen[94] θ as input GMN [9] θ as input MetaGAN[139] θ as input Attention PixelCNN[92] θ as input class this combination belo”
Meta-Transfer Learning Through Hard Tasks (2019, background): “However, this data generator often under-performs when trained on few-shot data, which has been investigated by [15].”
Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation (2021, methodology): “In this regard, some of the few-shot generative models have utilized meta-learning for improved generalization (Rezende et al., 2016; Bartunov & Vetrov, 2018; Clouˆatre & Demers, 2019).”
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning (2020, background): “classification problem, such as works based on graph theories [66], [67], [68], [69], reinforcement learning [70], differentiable SVM [71], generative models [72], [73], [74], [75], [76], [77], [78], [79], [80], transductive learning [81], [82], [83], [84], [85], recurrent models [86], [87], self-su”
Watch Your Up-Convolution: CNN Based Generative Deep Neural Networks Are Failing to Reproduce Spectral Distributions (2020, background): “…images [29, 6], image-to-image [45, 26, 61, 9, 42, 30] and text-to-image translations [48, 11, 58, 59], style transfer [27, 60, 61, 25], image inpainting [45, 54, 33, 26, 56], transfer learning [5, 10, 15] or even for training semantic segmentation tasks [35, 53], just to name a few.”
Consistent Generative Query Networks (2018, background): “Meta learning is often associated with few-shot classification tasks (Vinyals et al., 2016; Koch et al., 2015) but these algorithms can be extended to few-shot density estimation for image generation (Bartunov and Vetrov, 2018).”
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers (2020, unknown): “[4, 12, 14, 20, 27,28,37,45,49,57,60,61,65,69–71,76,77].”
FIGR: Few-shot Image Generation with Reptile (2019, background): “Bartunov and Vetrov (2018) [4] uses matching networks to achieve few-shot image generation.”