Impact of a DCT-driven Loss in Attention-based Knowledge-Distillation for Scene Recognition

• URL: http://arxiv.org/abs/2205.01997v1
• Date: Wed, 4 May 2022 11:05:18 GMT
• Title: Impact of a DCT-driven Loss in Attention-based Knowledge-Distillation for Scene Recognition
• Authors: Alejandro L\'opez-Cifuentes, Marcos Escudero-Vi\~nolo, Jes\'us Besc\'os and Juan C. SanMiguel
• Abstract summary: We propose and analyse the use of a 2D frequency transform of the activation maps before transferring them. This strategy enhances knowledge transferability in tasks such as scene recognition. We publicly release the training and evaluation framework used along this paper at http://www.vpu.eps.uam.es/publications/DCTBasedKDForSceneRecognition.
• Score: 64.29650787243443
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Knowledge Distillation (KD) is a strategy for the definition of a set of transferability gangways to improve the efficiency of Convolutional Neural Networks. Feature-based Knowledge Distillation is a subfield of KD that relies on intermediate network representations, either unaltered or depth-reduced via maximum activation maps, as the source knowledge. In this paper, we propose and analyse the use of a 2D frequency transform of the activation maps before transferring them. We pose that\textemdash by using global image cues rather than pixel estimates, this strategy enhances knowledge transferability in tasks such as scene recognition, defined by strong spatial and contextual relationships between multiple and varied concepts. To validate the proposed method, an extensive evaluation of the state-of-the-art in scene recognition is presented. Experimental results provide strong evidences that the proposed strategy enables the student network to better focus on the relevant image areas learnt by the teacher network, hence leading to better descriptive features and higher transferred performance than every other state-of-the-art alternative. We publicly release the training and evaluation framework used along this paper at http://www-vpu.eps.uam.es/publications/DCTBasedKDForSceneRecognition.

Related papers

• Harmonizing knowledge Transfer in Neural Network with Unified Distillation [20.922545937770085]
  Knowledge distillation (KD) is known for its ability to transfer knowledge from a cumbersome network (teacher) to a lightweight one (student) without altering the architecture. This paper introduces a novel perspective by leveraging diverse knowledge sources within a unified KD framework.
  arXiv  Detail & Related papers  (2024-09-27T09:09:45Z)
• Apprenticeship-Inspired Elegance: Synergistic Knowledge Distillation Empowers Spiking Neural Networks for Efficient Single-Eye Emotion Recognition [53.359383163184425]
  We introduce a novel multimodality synergistic knowledge distillation scheme tailored for efficient single-eye motion recognition tasks. This method allows a lightweight, unimodal student spiking neural network (SNN) to extract rich knowledge from an event-frame multimodal teacher network.
  arXiv  Detail & Related papers  (2024-06-20T07:24:47Z)
• Weakly Supervised Change Detection via Knowledge Distillation and Multiscale Sigmoid Inference [26.095501974608908]
  We develop a novel weakly supervised change detection technique via Knowledge Distillation and Multiscale Sigmoid Inference (KD-MSI) Our proposed technique, with its integrated training strategy, significantly outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2024-03-09T05:01:51Z)
• Feature-domain Adaptive Contrastive Distillation for Efficient Single Image Super-Resolution [3.2453621806729234]
  CNN-based SISR has numerous parameters and high computational cost to achieve better performance. Knowledge Distillation (KD) transfers teacher's useful knowledge to student. We propose a feature-domain adaptive contrastive distillation (FACD) method for efficiently training lightweight student SISR networks.
  arXiv  Detail & Related papers  (2022-11-29T06:24:14Z)
• Learning Knowledge Representation with Meta Knowledge Distillation for Single Image Super-Resolution [82.89021683451432]
  We propose a model-agnostic meta knowledge distillation method under the teacher-student architecture for the single image super-resolution task. Experiments conducted on various single image super-resolution datasets demonstrate that our proposed method outperforms existing defined knowledge representation related distillation methods.
  arXiv  Detail & Related papers  (2022-07-18T02:41:04Z)
• Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis [54.94682858474711]
  Class Activation Mapping (CAM) approaches provide an effective visualization by taking weighted averages of the activation maps. We propose a novel set of metrics to quantify explanation maps, which show better effectiveness and simplify comparisons between approaches.
  arXiv  Detail & Related papers  (2021-04-20T21:34:24Z)
• Joint Learning of Neural Transfer and Architecture Adaptation for Image Recognition [77.95361323613147]
  Current state-of-the-art visual recognition systems rely on pretraining a neural network on a large-scale dataset and finetuning the network weights on a smaller dataset. In this work, we prove that dynamically adapting network architectures tailored for each domain task along with weight finetuning benefits in both efficiency and effectiveness. Our method can be easily generalized to an unsupervised paradigm by replacing supernet training with self-supervised learning in the source domain tasks and performing linear evaluation in the downstream tasks.
  arXiv  Detail & Related papers  (2021-03-31T08:15:17Z)
• Knowledge Distillation Meets Self-Supervision [109.6400639148393]
  Knowledge distillation involves extracting "dark knowledge" from a teacher network to guide the learning of a student network. We show that the seemingly different self-supervision task can serve as a simple yet powerful solution. By exploiting the similarity between those self-supervision signals as an auxiliary task, one can effectively transfer the hidden information from the teacher to the student.
  arXiv  Detail & Related papers  (2020-06-12T12:18:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.