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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Updated: Aug 3, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Robust and Rotation-Equivariant Contrastive Learning.

Gairui Bai, Wei Xi, Xiaopeng Hong

    IEEE Transactions on Neural Networks and Learning Systems
    |April 7, 2023
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    Summary
    This summary is machine-generated.

    This study introduces RefosNet, enhancing contrastive learning (CL) with rotation transformations for robust image representation. RefosNet improves recognition accuracy, especially with unseen object orientations.

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    Area of Science:

    • Computer Vision
    • Machine Learning
    • Deep Learning

    Background:

    • Contrastive learning (CL) excels at learning invariant representations from image transformations.
    • Rotation transformations are typically excluded from CL, leading to poor performance with unseen orientations.

    Purpose of the Study:

    • To improve the robustness of CL methods against rotation transformations.
    • To enhance representation generalization for objects in both seen and unseen orientations.

    Main Methods:

    • Proposed Representation Focus Shift Network (RefosNet) incorporating rotation transformations into CL.
    • Constructed rotation-equivariant mapping between original and rotated image features.
    • Decoupled rotation-invariant and rotation-equivariant features to learn semantic-invariant representations (SIRs).
    • Introduced an adaptive gradient passivation strategy to manage representation focus shift and prevent catastrophic forgetting.

    Main Results:

    • RefosNet significantly improved recognition accuracy on datasets with unseen orientations (e.g., 7.12% on ObjectNet-13).
    • Performance gains were observed on datasets with seen orientations (e.g., 5.5% on ImageNet-100, 7.29% on STL10).
    • Demonstrated strong generalization capabilities on diverse datasets (Place205, PASCAL VOC, Caltech 101) and satisfactory results in image retrieval.

    Conclusions:

    • RefosNet effectively integrates rotation transformations into CL, enhancing representation robustness and generalization.
    • The proposed methods address limitations of existing CL approaches concerning object orientation variations.
    • RefosNet offers a promising direction for developing more versatile and accurate computer vision models.