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Found 5 papers, 4 papers with code
MTLoRA: A Low-Rank Adaptation Approach for Efficient Multi-Task Learning
Adapting models pre-trained on large-scale datasets to a variety of downstream tasks is a common strategy in deep learning.
AdaMTL: Adaptive Input-dependent Inference for Efficient Multi-Task Learning
Specifically, we attach a task-aware lightweight policy network to the shared encoder and co-train it alongside the MTL model to recognize unnecessary computations.
BitTrain: Sparse Bitmap Compression for Memory-Efficient Training on the Edge
However, memory footprint from activations is the main bottleneck for training on the edge.
AdaCon: Adaptive Context-Aware Object Detection for Resource-Constrained Embedded Devices
In particular, our technique clusters the object categories based on their spatial co-occurrence probability.
AdaSense: Adaptive Low-Power Sensing and Activity Recognition for Wearable Devices
The proposed techniques reduce the power consumption by dynamically switching among different sensor configurations as a function of the user activity.
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