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Found 3 papers, 2 papers with code
Overcoming Oscillations in Quantization-Aware Training
These effects are particularly pronounced in low-bit ($\leq$ 4-bits) quantization of efficient networks with depth-wise separable layers, such as MobileNets and EfficientNets.
Understanding and Overcoming the Challenges of Efficient Transformer Quantization
Finally, we show that transformer weights and embeddings can be quantized to ultra-low bit-widths, leading to significant memory savings with a minimum accuracy loss.
A White Paper on Neural Network Quantization
Neural network quantization is one of the most effective ways of achieving these savings but the additional noise it induces can lead to accuracy degradation.
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