On the Failure of Boundary-Seeking Distillation in Bottlenecked Generative Architectures
Data-free knowledge distillation transfers the knowledge encoded in a teacher model to a student model without access to the original training data. Prior work such as Contrastive Abductive Knowledge Extraction (CAKE) achieves this for classifiers by synthesizing samples near the teacher's decision boundary. In this work, we investigate whether this boundary-seeking principle extends to autoencoder distillation through experiments on the MNIST dataset . To enable a direct comparison, we reformul
Record details
Published: 17 July 2026
Source: arXiv cs.AI
Category: Research
Topics: Children & education · Finance, VC & PE
Retrieved: 20 July 2026
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ethics.ai (17 July 2026), “On the Failure of Boundary-Seeking Distillation in Bottlenecked Generative Architectures,” evidence record 11853, https://ethics.ai/record/11853 (originally published by arXiv cs.AI).
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