Learning Deep Visual Representations
type: Dataset
description: Recent advancements in the areas of deep learning and visual information processing have presented an opportunity to unite both fields. These complementary fields combine to tackle the problem of classifying images into their semantic categories. Deep learning brings learning and representational capabilities to a visual processing model that is adapted for image classification. This thesis addresses problems that lead to the proposal of learning deep visual representations for image classification. The problem of deep learning is tackled on two fronts. The first aspect is the problem of unsupervised learning of latent representations from input data. The main focus is the integration of prior knowledge into the learning of restricted Boltzmann machines (RBM) through regularization. Regularizers are proposed to induce sparsity, selectivity and topographic organization in the coding to improve discrimination and invariance. The second direction introduces the notion of gradually transiting from unsupervised layer-wise learning to supervised deep learning. This is done through the integration of bottom-up information with top-down signals. Two novel implementations supporting this notion are explored. The first method uses top-down regularization to train a deep network of RBMs. The second method combines predictive and reconstructive loss functions to optimize a stack of encoder-decoder networks. The proposed deep learning techniques are applied to tackle the image classification problem. The bag-of-words model is adopted due to its strengths in image modeling through the use of local image descriptors and spatial pooling schemes. Deep learning with spatial aggregation is used to learn a hierarchical visual dictionary for encoding the image descriptors into mid-level representations. This method achieves leading image classification performances for object and scene images. The learned dictionaries are diverse and non-redundant. The speed of inference is also high. F…
issued: 2026-05-06T08:28:46.311598
language: en
modified: 2026-05-06T08:28:46.311602
contact point: Organization
  fn: CCSD
keyword: apprentissage-profond
keyword: classification-dimages
keyword: deep-learning
keyword: hierarchical-visual-representations
keyword: image-classification
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-cvcomputer-science-cscomputer-vision-and-pattern-recognition-cscv
keyword: infoinfo-lgcomputer-science-csmachine-learning-cslg
keyword: representations-visuelles-hierarchiques
keyword: theses
landing page: https://theses.hal.science/tel-00948376

Learning Deep Visual Representations
type: Distribution
format: HTML
issued: 2026-05-06T08:28:46.313753
modified: 2026-05-06T08:28:46.292100
access URL: https://theses.hal.science/tel-00948376

test_moissonnage_selune
type: Agent