Learned Gaussian ProtoNet for improved cross-domain few-shot classification and generalization

To imitate intelligent human behaviour, computer vision introduces a fundamental task called Few-Shot learning (FSL) that carries the promise of alleviating the need for exhaustively labeled data. Using prior knowledge few-shot learning aims to learn and generalize to novel tasks containing limited...

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Bibliographic Details
Published in:Neural computing & applications 2023-02, Vol.35 (4), p.3435-3448
Main Authors: Khanday, Nadeem Yousuf, Sofi, Shabir Ahmad
Format: Article
Language:English
Subjects:
Artificial Intelligence
Classification
Coders
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer vision
Covariance matrix
Data Mining and Knowledge Discovery
Data points
Domains
Embedding
Image Processing and Computer Vision
Learning
Original Article
Performance enhancement
Probability and Statistics in Computer Science
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Summary:To imitate intelligent human behaviour, computer vision introduces a fundamental task called Few-Shot learning (FSL) that carries the promise of alleviating the need for exhaustively labeled data. Using prior knowledge few-shot learning aims to learn and generalize to novel tasks containing limited examples with supervised information. Although metric-based methods demonstrated promising performance but due to the large disparity of feature distributions across domains they often fail to generalize. In this work, we propose a learned Gaussian ProtoNet model for fine-grained few-shot classification via meta-learning for both in-domain and cross-domain scenarios. Gaussian ProtoNet encoder helps to map an image into an embedding vector and Gaussian covariance matrix predicts the confidence region about individual data points. Direction and class-dependent distance metrics are adopted to estimate the distances to distinct class prototypes. Feature-wise modulated layers are embedded in the encoder to augment the feature distribution of images. The learning-to-learn approach is adopted for fine-tuning the hyper-parameters of incorporated feature-wise modulated layers for better generalization on unseen domains. Experimental results justify that our proposed model performs better than many state-of-the-art models and feature-wise modulation improves the performance under domain shifts.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-022-07897-9