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Found 5 papers, 0 papers with code
Clustering-based Domain-Incremental Learning
A key challenge in this setting is the so-called "catastrophic forgetting problem", in which the performance of the learner in an "old task" decreases when subsequently trained on a "new task".
A Linearly Convergent GAN Inversion-based Algorithm for Reverse Engineering of Deceptions
In this paper, we build on prior work and propose a novel framework for reverse engineering of deceptions which supposes that the clean data lies in the range of a GAN.
Reverse Engineering $\ell_p$ attacks: A block-sparse optimization approach with recovery guarantees
We pose this problem as a block-sparse recovery problem, where both the signal and the attack are assumed to lie in a union of subspaces that includes one subspace per class and one subspace per attack type.
Implicit Bias of Projected Subgradient Method Gives Provable Robust Recovery of Subspaces of Unknown Codimension
In particular, we show that 1) all of the problem instances will converge to a vector in the null space of the subspace and 2) the ensemble of problem instance solutions will be sufficiently diverse to fully span the null space of the subspace (and thus reveal the true codimension of the subspace) even when the true subspace dimension is unknown.
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing
In a plethora of applications dealing with inverse problems, e. g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest is known to be structured in several ways at the same time.
