Learning Sparse FRAME Models for Natural Image Patterns

49 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning Energy-Based Spatial-Temporal Generative ConvNets for Dynamic Patterns (2019, methodology): “Our previous works [34] and [35] have studied this issue.”
Learning Descriptor Networks for 3D Shape Synthesis and Analysis (2018, methodology): “The sparse FRAME model [30, 32], which was used for modeling object patterns.”
A Theory of Generative ConvNet (2016, neutral): “See also (Xie et al., 2014).”
Learning FRAME Models Using CNN Filters (2015, methodology): “In the paper of Xie et al. (2015a) , they use h ( r ) = | r | for full wave rectiﬁcation.”
Deep Cocktail Network: Multi-source Unsupervised Domain Adaptation with Category Shift (2018, methodology): “In our experiment, we introduce two shallow meth-ods, sparse FRAME ( sFRAME ) [46] and SGF [16] as the multi-source baselines in the Ofﬁce-31 experiment.”
Inducing wavelets into random fields via generative boosting (2016, unknown): “We show that Gibbs sampling is less prone to local modes 140 than the Hamiltonian Monte Carlo sampler employed in [24].”
From BoW to CNN: Two Decades of Texture Representation for Texture Classification (2018, background): “At the end of the last century there was a renaissance of texton-based approaches, including Zhu et al. [248, 249, 270, 271, 269, 272] on the mathematical modelling of textures and textons.”