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Variational Network Quantization
In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem.
Mind the Gap Between Synthetic and Real: Utilizing Transfer Learning to Probe the Boundaries of Stable Diffusion Generated Data
Building upon our insights that mainly later layers are responsible for the drop, we investigate the data-efficiency of fine-tuning a synthetically trained model with real data applied to only those last layers.
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