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Found 5 papers, 1 papers with code
Learning with Diversity: Self-Expanded Equalization for Better Generalized Deep Metric Learning
Most existing DML methods focus on improving the model robustness against category shift to keep the performance on unseen categories.
Noise Is Also Useful: Negative Correlation-Steered Latent Contrastive Learning
While in metric space, we utilize weakly-supervised contrastive learning to excavate these negative correlations hidden in noisy data.
Adaptive Hierarchical Similarity Metric Learning with Noisy Labels
Since these noisy labels often cause severe performance degradation, it is crucial to enhance the robustness and generalization ability of DML.
Domain-Smoothing Network for Zero-Shot Sketch-Based Image Retrieval
Most existing methods regard ZS-SBIR as a traditional classification problem and employ a cross-entropy or triplet-based loss to achieve retrieval, which neglect the problems of the domain gap between sketches and natural images and the large intra-class diversity in sketches.
Unsupervised Hyperbolic Metric Learning
Learning feature embedding directly from images without any human supervision is a very challenging and essential task in the field of computer vision and machine learning.
