4 research outputs found
Lecture notes: Semidefinite programs and harmonic analysis
Lecture notes for the tutorial at the workshop HPOPT 2008 - 10th
International Workshop on High Performance Optimization Techniques (Algebraic
Structure in Semidefinite Programming), June 11th to 13th, 2008, Tilburg
University, The Netherlands.Comment: 31 page
Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations
An important line of research attempts to explain CNN image classifier
predictions and intermediate layer representations in terms of human
understandable concepts. In this work, we expand on previous works in the
literature that use annotated concept datasets to extract interpretable feature
space directions and propose an unsupervised post-hoc method to extract a
disentangling interpretable basis by looking for the rotation of the feature
space that explains sparse one-hot thresholded transformed representations of
pixel activations. We do experimentation with existing popular CNNs and
demonstrate the effectiveness of our method in extracting an interpretable
basis across network architectures and training datasets. We make extensions to
the existing basis interpretability metrics found in the literature and show
that, intermediate layer representations become more interpretable when
transformed to the bases extracted with our method. Finally, using the basis
interpretability metrics, we compare the bases extracted with our method with
the bases derived with a supervised approach and find that, in one aspect, the
proposed unsupervised approach has a strength that constitutes a limitation of
the supervised one and give potential directions for future research.Comment: 15 pages, Accepted in IEEE Transactions on Artificial Intelligence,
Special Issue on New Developments in Explainable and Interpretable A
Generalized Neural Collapse for a Large Number of Classes
Neural collapse provides an elegant mathematical characterization of learned
last layer representations (a.k.a. features) and classifier weights in deep
classification models. Such results not only provide insights but also motivate
new techniques for improving practical deep models. However, most of the
existing empirical and theoretical studies in neural collapse focus on the case
that the number of classes is small relative to the dimension of the feature
space. This paper extends neural collapse to cases where the number of classes
are much larger than the dimension of feature space, which broadly occur for
language models, retrieval systems, and face recognition applications. We show
that the features and classifier exhibit a generalized neural collapse
phenomenon, where the minimum one-vs-rest margins is maximized.We provide
empirical study to verify the occurrence of generalized neural collapse in
practical deep neural networks. Moreover, we provide theoretical study to show
that the generalized neural collapse provably occurs under unconstrained
feature model with spherical constraint, under certain technical conditions on
feature dimension and number of classes.Comment: 32 pages, 12 figure