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Task-Specific Preconditioner for Cross-Domain Few-Shot Learning
Suhyun Kang, Jungwon Park, Wonseok Lee +1
Cross-Domain Few-Shot Learning~(CDFSL) methods typically parameterize models with task-agnostic and task-specific parameters. To adapt task-specific parameters, recent approaches h…
Towards a Better Evaluation of Out-of-Domain Generalization
Duhun Hwang, Suhyun Kang, Moonjung Eo +2
The objective of Domain Generalization (DG) is to devise algorithms and models capable of achieving high performance on previously unseen test distributions. In the pursuit of this…
A Differentiable Framework for End-to-End Learning of Hybrid Structured Compression
Moonjung Eo, Suhyun Kang, Wonjong Rhee
Filter pruning and low-rank decomposition are two of the foundational techniques for structured compression. Although recent efforts have explored hybrid approaches aiming to integ…
The Geometric Occam's Razor Implicit in Deep Learning
Benoit Dherin, Michael Munn, David G. T. Barrett
In over-parameterized deep neural networks there can be many possible parameter configurations that fit the training data exactly. However, the properties of these interpolating so…