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Found 3 papers, 2 papers with code
Semi-Supervised Semantic Segmentation via Marginal Contextual Information
We present a novel confidence refinement scheme that enhances pseudo-labels in semi-supervised semantic segmentation.
FBM: Fast-Bit Allocation for Mixed-Precision Quantization
Quantized neural networks are well known for reducing latency, power consumption, and model size without significant degradation in accuracy, making them highly applicable for systems with limited resources and low power requirements.
Bimodal Distributed Binarized Neural Networks
The proposed method consists of a training scheme that we call Weight Distribution Mimicking (WDM), which efficiently imitates the full-precision network weight distribution to their binary counterpart.
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