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Found 5 papers, 3 papers with code
Butterfly Transform: An Efficient FFT Based Neural Architecture Design
By replacing pointwise convolutions with BFT, we reduce the computational complexity of these layers from O(n^2) to O(n\log n) with respect to the number of channels.
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks
In this paper, we perform an empirical evaluation on methods for sharing parameters in isotropic networks (SPIN).
LCS: Learning Compressible Subspaces for Adaptive Network Compression at Inference Time
Our models require no retraining, thus our subspace of models can be deployed entirely on-device to allow adaptive network compression at inference time.
Token Pooling in Vision Transformers
Token Pooling is a simple and effective operator that can benefit many architectures.
On the Efficacy of Multi-scale Data Samplers for Vision Applications
Multi-scale resolution training has seen an increased adoption across multiple vision tasks, including classification and detection.
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