Deep Tensor Encoding
Learning an encoding of feature vectors in terms of an over-complete dictionary or a information geometric (Fisher vectors) construct is wide-spread in statistical signal processing and computer vision. In content based information retrieval using deep-learning classifiers, such encodings are learnt on the flattened last layer, without adherence to the multi-linear structure of the underlying feature tensor. We illustrate a variety of feature encodings incl. sparse dictionary coding and Fisher vectors along with proposing that a structured tensor factorization scheme enables us to perform retrieval that can be at par, in terms of average precision, with Fisher vector encoded image signatures. In short, we illustrate how structural constraints increase retrieval fidelity.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalRetrievalSimilar Papers 제목 키워드 기반
ReLATE: Accelerating Tensor Decomposition via Safe and Efficient Learning of Sparse Encodings
Tensor decomposition (TD) is essential for analyzing high-dimensional sparse data, yet its irregular computations and memory-access patterns pose major performance challenges on modern parallel processors. Prior works re…
Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding
Encoding classical data into quantum states is a central bottleneck in quantum machine learning: many widely used encodings are circuit-inefficient, requiring deep circuits and substantial quantum resources, which limits…
Quantum Machine LearningFlexDTI: Flexible diffusion gradient encoding scheme-based highly efficient diffusion tensor imaging using deep learning
Objective: Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients' number and directions in the data to be reconstructed to match those in the training data. This work aims to dev…
parameter estimationTensor Convolutional Sparse Coding with Low-Rank activations, an application to EEG analysis
Recently, there has been growing interest in the analysis of spectrograms of ElectroEncephaloGram (EEG), particularly to study the neural correlates of (un)-consciousness during General Anesthesia (GA). Indeed, it has be…
EEGElectroencephalogram (EEG)On embedding Lambek calculus into commutative categorial grammars
We consider tensor grammars, which are an example of \commutative" grammars, based on the classical (rather than intuitionistic) linear logic. They can be seen as a surface representation of abstract categorial grammars …
Translation