AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Quantizer-Aligned Recalibration (QuAR), a single-pass TTA method tailored to quantized ViTs that neither backpropagates nor updates any model parameters is proposed, which achieves the highest mean accuracy among state-of-the-art backpropagates nor updates any model parameters.
Although compressed models retain high accuracy under supervised adaptation, their TTA performance degrades significantly with increasing compression, highlighting the need to design compression strategies that preserve adaptability.
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