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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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A Comprehensive Framework for Long-Tailed Learning via Pretraining and Normalization.

Nan Kang, Hong Chang, Bingpeng Ma

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    This study enhances deep learning for imbalanced visual data by improving contrastive pretraining and introducing a novel generalized normalization classifier. These methods boost recognition for underrepresented classes without harming performance on common classes.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Visual data often exhibits long-tailed distributions, posing challenges for deep learning models in learning robust representations and accurate classifiers.
    • Existing self-supervised pretraining methods for long-tailed learning are suboptimal in terms of performance and speed.
    • Normalization techniques for classifiers require further investigation for imbalanced datasets.

    Purpose of the Study:

    • To improve feature extraction for long-tailed recognition using enhanced contrastive pretraining.
    • To develop a more effective classifier for imbalanced data through novel normalization strategies.
    • To create a unified framework that achieves state-of-the-art performance on long-tailed recognition benchmarks efficiently.

    Main Methods:

    • Proposed a balanced contrastive loss and a fast contrastive initialization scheme to optimize self-supervised pretraining for long-tailed data.
    • Introduced a generalized normalization classifier featuring grouped learnable scaling, outperforming standard inner product and cosine classifiers.
    • Integrated these components into a unified framework for comprehensive long-tailed recognition.

    Main Results:

    • The proposed balanced contrastive loss and fast initialization significantly improve contrastive pretraining for long-tailed learning.
    • The generalized normalization classifier demonstrates superior performance, particularly for tail classes, without compromising head class accuracy.
    • The unified framework achieves competitive results on multiple long-tailed recognition benchmarks while maintaining high efficiency.

    Conclusions:

    • The developed contrastive pretraining and generalized normalization classifier effectively address the challenges of long-tailed visual data.
    • The proposed methods enhance recognition capabilities for minority classes, offering a significant advancement in imbalanced learning.
    • The unified framework provides an efficient and high-performing solution for real-world long-tailed recognition tasks.