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Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation
- Publication Year :
- 2022
- Publisher :
- arXiv, 2022.
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Abstract
- Recent neural methods for vehicle routing problems always train and test the deep models on the same instance distribution (i.e., uniform). To tackle the consequent cross-distribution generalization concerns, we bring the knowledge distillation to this field and propose an Adaptive Multi-Distribution Knowledge Distillation (AMDKD) scheme for learning more generalizable deep models. Particularly, our AMDKD leverages various knowledge from multiple teachers trained on exemplar distributions to yield a light-weight yet generalist student model. Meanwhile, we equip AMDKD with an adaptive strategy that allows the student to concentrate on difficult distributions, so as to absorb hard-to-master knowledge more effectively. Extensive experimental results show that, compared with the baseline neural methods, our AMDKD is able to achieve competitive results on both unseen in-distribution and out-of-distribution instances, which are either randomly synthesized or adopted from benchmark datasets (i.e., TSPLIB and CVRPLIB). Notably, our AMDKD is generic, and consumes less computational resources for inference.<br />Comment: Accepted at NeurIPS 2022
Details
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....b2b9895e46a94914360b39158c16ec13
- Full Text :
- https://doi.org/10.48550/arxiv.2210.07686