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Found 5 papers, 0 papers with code
Boosting Box-supervised Instance Segmentation with Pseudo Depth
This innovative approach empowers the network to simultaneously predict masks and depth, enhancing its ability to capture nuanced depth-related information during the instance segmentation process.
Conditional Generative Data-free Knowledge Distillation
In this paper, we propose a conditional generative data-free knowledge distillation (CGDD) framework for training lightweight networks without any training data.
Conditional Image Generation
Data-free Knowledge Distillation
+1
Markdowns in E-Commerce Fresh Retail: A Counterfactual Prediction and Multi-Period Optimization Approach
In this paper, by leveraging abundant observational transaction data, we propose a novel data-driven and interpretable pricing approach for markdowns, consisting of counterfactual prediction and multi-period price optimization.
A Unified Framework for Marketing Budget Allocation
In our approach, a semi-black-box model is built to forecast the dynamic market response and an efficient optimization method is proposed to solve the complex allocation task.
Distributed Power-law Graph Computing: Theoretical and Empirical Analysis
We theoretically prove that DBH can achieve lower communication cost than existing methods and can simultaneously guarantee good workload balance.
