Invariant Causal Knowledge Distillation in Neural Networks
<|reference_start|>Invariant Causal Knowledge Distillation in Neural Networks: Knowledge distillation (KD) involves transferring the knowledge from one neural network to another, often from a larger, well-trained model (teacher) to a smaller, more efficient model (student). Traditional KD methods minimize the Kullback-Leibler (KL) divergence between the probabilistic outputs of the teacher and student networks. However, this approach often overlooks crucial structural knowledge embedded within the teacher's network. In this paper, we introduce Invariant Consistency Distillation (ICD), a novel methodology designed to enhance KD by ensuring that the student model's representations are both discriminative and invariant with respect to the teacher's outputs. Our approach is based on causal inference principles and combines contrastive learning with an explicit invariance penalty, capturing significantly more information from the teacher's representation. ICD uses an efficient, parameter-free approach for flexible teacher-student alignment. We provide a theoretical foundation for ICD and demonstrate its effectiveness through extensive experiments. Our results on CIFAR-100 and ImageNet ILSVRC-2012 show that ICD outperforms traditional KD techniques and surpasses state-of-the-art methods. In some cases, the student model even exceeds the teacher model in terms of accuracy. Furthermore, we successfully apply our method to other datasets, such as Tiny ImageNet and STL-10, demonstrating superior cross-dataset generalization. Code is available at https://github.com/giakoumoglou/distillers.<|reference_end|>
@article{giakoumoglou2024invariant,
    title={Invariant Causal Knowledge Distillation in Neural Networks},
    author={Nikolaos Giakoumoglou, Tania Stathaki},
    journal={arXiv preprint arXiv:2407.11802},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.11802},
    primaryClass={cs.CV cs.AI}
}