Tensor-Train decomposition for image recognition
نویسندگان
چکیده
منابع مشابه
A Randomized Tensor Train Singular Value Decomposition
The hierarchical SVD provides a quasi-best low rank approximation of high dimensional data in the hierarchical Tucker framework. Similar to the SVD for matrices, it provides a fundamental but expensive tool for tensor computations. In the present work we examine generalizations of randomized matrix decomposition methods to higher order tensors in the framework of the hierarchical tensors repres...
متن کاملTensor Train decomposition on TensorFlow (T3F)
Tensor Train decomposition is used across many branches of machine learning, but until now it lacked an implementation with GPU support, batch processing, automatic differentiation, and versatile functionality for Riemannian optimization framework, which takes in account the underlying manifold structure in order to construct efficient optimization methods. In this work, we propose a library th...
متن کاملFisher Tensor Decomposition for Unconstrained Gait Recognition
This paper proposes a simplified Tucker decomposition of a tensor model for gait recognition from dense local spatiotemporal (S/T) features extracted from gait video sequences. Unlike silhouettes, local S/T features have displayed state-of-art performances on challenging action recognition testbeds, and have the potential to push gait ID towards real-world deployment. We adopt a Fisher represen...
متن کاملTensor Networks for Latent Variable Analysis. Part I: Algorithms for Tensor Train Decomposition
Decompositions of tensors into factor matrices, which interact through a core tensor, have found numerous applications in signal processing and machine learning. A more general tensor model which represents data as an ordered network of sub-tensors of order-2 or order-3 has, so far, not been widely considered in these fields, although this so-called tensor network decomposition has been long st...
متن کاملCompressing Bidirectional Texture Functions via Tensor Train Decomposition
Material reflectance properties play a central role in photorealistic rendering. Bidirectional texture functions (BTFs) can faithfully represent these complex properties, but their inherent high dimensionality (texture coordinates, color channels, view and illumination spatial directions) requires many coefficients to encode. Numerous algorithms based on tensor decomposition have been proposed ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Calcolo
سال: 2020
ISSN: 0008-0624,1126-5434
DOI: 10.1007/s10092-020-0358-8