227 research outputs found
Variable Weighted Ordered Subset Image Reconstruction Algorithm
We propose two variable weighted iterative reconstruction algorithms (VW-ART and VW-OS-SART) to improve the algebraic reconstruction technique (ART) and simultaneous algebraic reconstruction technique (SART) and establish their convergence. In the two algorithms, the weighting varies with the geometrical direction of the ray. Experimental results with both numerical simulation and real CT data demonstrate that the VW-ART has a significant improvement in the quality of reconstructed images over ART and OS-SART. Moreover, both VW-ART and VW-OS-SART are more promising in convergence speed than the ART and SART, respectively
Regeneration Learning: A Learning Paradigm for Data Generation
Machine learning methods for conditional data generation usually build a
mapping from source conditional data X to target data Y. The target Y (e.g.,
text, speech, music, image, video) is usually high-dimensional and complex, and
contains information that does not exist in source data, which hinders
effective and efficient learning on the source-target mapping. In this paper,
we present a learning paradigm called regeneration learning for data
generation, which first generates Y' (an abstraction/representation of Y) from
X and then generates Y from Y'. During training, Y' is obtained from Y through
either handcrafted rules or self-supervised learning and is used to learn
X-->Y' and Y'-->Y. Regeneration learning extends the concept of representation
learning to data generation tasks, and can be regarded as a counterpart of
traditional representation learning, since 1) regeneration learning handles the
abstraction (Y') of the target data Y for data generation while traditional
representation learning handles the abstraction (X') of source data X for data
understanding; 2) both the processes of Y'-->Y in regeneration learning and
X-->X' in representation learning can be learned in a self-supervised way
(e.g., pre-training); 3) both the mappings from X to Y' in regeneration
learning and from X' to Y in representation learning are simpler than the
direct mapping from X to Y. We show that regeneration learning can be a
widely-used paradigm for data generation (e.g., text generation, speech
recognition, speech synthesis, music composition, image generation, and video
generation) and can provide valuable insights into developing data generation
methods
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