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Post-Training Quantization
Technique applied after model training to reduce weight precision without retraining.
Quantification Sensible à la Distribution
Approche adaptant les niveaux de quantification selon la distribution statistique des poids du réseau.
Quantization-Aware Training
Method integrating quantization simulation during training to minimize precision loss.
Mixed Quantization
Strategy using different bit precisions for different layers of the neural network.
Variable Bit Quantization
Technique dynamically optimizing the number of bits allocated to each weight or activation.
Non-Uniform Quantization
Approach using uneven quantization intervals to better represent the weight distribution.
Dynamic Quantization
Method computing quantization parameters at runtime for each input batch.
Static Quantization
Process of precomputing quantization parameters before inference to optimize performance.
Extreme Quantization
Technique pushing precision reduction down to 1-2 bits for maximum compression.
Quantization by Clustering
Approach grouping similar weights into clusters to optimize the quantized representation.
Structurally Constrained Quantization
Method preserving the network structure while applying specific quantization constraints.
Adaptive Quantization
Technique automatically adjusting the quantization strategy according to the characteristics of the model and data.