Difficulty Diversity and Plausibility: Dynamic Data-Free Quantization

Cheeun Hong, Sungyong Baik, Junghun Oh, Kyoung Mu Lee; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 7531-7540

Without access to the original training data data-free quantization (DFQ) aims to recover the performance loss induced by quantization. Most previous works have focused on using an original network to extract the train data information which is instilled into surrogate synthesized images. However existing DFQ methods do not take into account important aspects of quantization: the extent of a computational-cost-and-accuracy trade-off varies for each image depending on its task difficulty. To handle such varying trade-offs several efforts have been made to dynamically allocate bit-widths for each image. Such dynamic quantization however remains challenging and unexplored in the data-free domain because synthesized images of previous works fail to possess properties in natural test images that are crucial for learning the appropriate dynamic allocation policy: difficulty its diversity and its plausibility. By contrast we propose a data-free quantization framework that is dynamic-friendly by modeling varying extents of task difficulties with plausibility. We generate plausibly difficult images with soft labels whose probabilities are allocated to a group of similar classes. Images with diverse and plausible difficulties enable us to train the framework to dynamically handle the varying trade-offs. Consequently our framework achieves better accuracy-complexity Pareto front than existing data-free quantization approaches.