Intelligent metasurfaces: control, communication and computing
Controlling electromagnetic waves and information simultaneously by information
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …
metasurfaces is of central importance in modern society. Intelligent metasurfaces are smart …
Inducing neural collapse in imbalanced learning: Do we really need a learnable classifier at the end of deep neural network?
Modern deep neural networks for classification usually jointly learn a backbone for
representation and a linear classifier to output the logit of each class. A recent study has …
representation and a linear classifier to output the logit of each class. A recent study has …
Understanding imbalanced semantic segmentation through neural collapse
A recent study has shown a phenomenon called neural collapse in that the within-class
means of features and the classifier weight vectors converge to the vertices of a simplex …
means of features and the classifier weight vectors converge to the vertices of a simplex …
Extended unconstrained features model for exploring deep neural collapse
The modern strategy for training deep neural networks for classification tasks includes
optimizing the network's weights even after the training error vanishes to further push the …
optimizing the network's weights even after the training error vanishes to further push the …
Imbalance trouble: Revisiting neural-collapse geometry
C Thrampoulidis, GR Kini… - Advances in Neural …, 2022 - proceedings.neurips.cc
Neural Collapse refers to the remarkable structural properties characterizing the geometry of
class embeddings and classifier weights, found by deep nets when trained beyond zero …
class embeddings and classifier weights, found by deep nets when trained beyond zero …
How far pre-trained models are from neural collapse on the target dataset informs their transferability
This paper focuses on model transferability estimation, ie, assessing the performance of pre-
trained models on a downstream task without performing fine-tuning. Motivated by the …
trained models on a downstream task without performing fine-tuning. Motivated by the …