In this paper, we propose an approach that builds on top of BoF pooling to boost its efficiency by ensuring that the items of the learned dictionary are non-redundant.
In this paper, we consider the problem of non-linear dimensionality reduction under uncertainty, both from a theoretical and algorithmic perspectives.
We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
Based on this idea, we propose to reformulate the attention mechanism in CNNs to learn to ignore instead of learning to attend.
We test this approach on the proposed method and show that it can indeed be used to avoid several extreme error cases and, thus, improves the practicality of the proposed technique.
Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one.
We study the diversity of the features learned by a two-layer neural network trained with the least squares loss.
Energy-based learning is a powerful learning paradigm that encapsulates various discriminative and generative approaches.
During the last decade, neural networks have been intensively used to tackle various problems and they have often led to state-of-the-art results.
spectral-based subspace learning is a common data preprocessing step in many machine learning pipelines.
Computational color constancy is a preprocessing step used in many camera systems.
In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC).
In this paper, we describe a new large dataset for illumination estimation.
To further improve the illumination estimation accuracy, we propose a novel attention mechanism for the BoCF model with two variants based on self-attention.
In this paper, we study the importance of pre-training for the generalization capability in the color constancy problem.