# Deep Tensor Encoding

## TL;DR

This paper proposes a structured tensor factorization approach for feature encoding in deep learning, demonstrating that incorporating multi-linear structure improves retrieval accuracy comparable to Fisher vectors.

## Contribution

It introduces a novel tensor factorization scheme that enforces structural constraints, enhancing retrieval performance over traditional flattened encodings.

## Key findings

- Structured tensor encoding achieves comparable average precision to Fisher vectors.
- Enforcing multi-linear structure increases retrieval fidelity.
- Various feature encodings like sparse coding and Fisher vectors are compared.

## Abstract

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.