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Found 3 papers, 1 papers with code
On the Demystification of Knowledge Distillation: A Residual Network Perspective
Surprisingly, in some cases, they surpass the accuracy of baseline networks even with the inferior teachers.
E2GC: Energy-efficient Group Convolution in Deep Neural Networks
We show that, at comparable computational complexity, DNNs with constant group size (E2GC) are more energy-efficient than DNNs with a fixed number of groups (F$g$GC).
ULSAM: Ultra-Lightweight Subspace Attention Module for Compact Convolutional Neural Networks
Our method of subspace attention is orthogonal and complementary to the existing state-of-the-arts attention mechanisms used in vision models.
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