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Found 4 papers, 2 papers with code
Learning Spatially-Adaptive Squeeze-Excitation Networks for Image Synthesis and Image Recognition
For image recognition tasks, the proposed SASE is used as a drop-in replacement for convolution layers in ResNets and achieves much better accuracy than the vanilla ResNets, and slightly better than the MHSA counterparts such as the Swin-Transformer and Pyramid-Transformer in the ImageNet-1000 dataset, with significantly smaller models.
Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity
For example, our model with only 1 layer of 15 trees can perform comparably with the model in [3] with 2 layers of 2000 trees each.
Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference
The core idea of DFS is to hypothesize layer-wise quantization (to different bitwidths) as intermediate "soft" choices to be made between fully utilizing and skipping a layer.
Dual Dynamic Inference: Enabling More Efficient, Adaptive and Controllable Deep Inference
State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications.
