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Found 5 papers, 1 papers with code
Value-Driven Mixed-Precision Quantization for Patch-Based Inference on Microcontrollers
In addition, for patches without outlier values, we utilize value-driven quantization search (VDQS) on the feature maps of their following dataflow branches to reduce search time.
GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection
This perspective is motivated by our observation that gradient-based attribution methods encounter challenges in assigning feature importance to OOD data, thereby yielding divergent explanation patterns.
EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices
In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem.
Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning
This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes.
Detecting Out-of-distribution Examples via Class-conditional Impressions Reappearing
Out-of-distribution (OOD) detection aims at enhancing standard deep neural networks to distinguish anomalous inputs from original training data.
