In the context of weakly-supervised learning, the WSDHQ framework can be adapted to leverage additional sources of information to further enhance quantization performance. One approach is to incorporate semi-supervised learning techniques by utilizing a small amount of labeled data along with the weakly labeled data. This can help in refining the quantization model and improving the quality of the learned representations. Furthermore, leveraging unlabeled data through techniques like self-supervised learning or unsupervised pre-training can provide valuable information for enhancing the quantization process. By incorporating these additional sources of information, the WSDHQ framework can improve its performance and robustness in weakly-supervised scenarios.