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Found 7 papers, 5 papers with code
LDReg: Local Dimensionality Regularized Self-Supervised Learning
Representations learned via self-supervised learning (SSL) can be susceptible to dimensional collapse, where the learned representation subspace is of extremely low dimensionality and thus fails to represent the full data distribution and modalities.
Dimensionality-Aware Outlier Detection: Theoretical and Experimental Analysis
We present a nonparametric method for outlier detection that takes full account of local variations in intrinsic dimensionality within the dataset.
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental Analysis
Accurate estimation of Intrinsic Dimensionality (ID) is of crucial importance in many data mining and machine learning tasks, including dimensionality reduction, outlier detection, similarity search and subspace clustering.
Subspace Determination through Local Intrinsic Dimensional Decomposition: Theory and Experimentation
For each point, the recently-proposed Local Intrinsic Dimension (LID) model is used in identifying the axis directions along which features have the greatest local discriminability, or equivalently, the fewest number of components of LID that capture the local complexity of the data.
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
In this paper, we demonstrate that an intrinsic dimensional characterization of the data space learned by a GAN model leads to an effective evaluation metric for GAN quality.
Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels present challenges for training accurate Deep Neural Networks (DNNs).
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Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality
Deep Neural Networks (DNNs) have recently been shown to be vulnerable against adversarial examples, which are carefully crafted instances that can mislead DNNs to make errors during prediction.
