Two new research papers propose methods to improve black-box knowledge distillation, a technique for compressing large AI models into smaller ones without direct access to the teacher model's training data. The first paper introduces a generative adversarial network scheme that adaptively selects high-confidence images to enhance diversity in the distillation set. The second paper presents a three-phase framework called DIP-KD, which synthesizes image priors, uses contrastive learning, and employs a primer student for distillation, also emphasizing data diversity. Both approaches report state-of-the-art results on various benchmarks. AI
影响 These methods could enable more efficient model compression in scenarios with limited data access, potentially lowering deployment costs for complex AI systems.
排序理由 Two academic papers published on arXiv propose novel methods for black-box knowledge distillation.
- arXiv
- Computer Vision
- DIP-KD
- Generative Adversarial Networks
- Knowledge Distillation
- Machine Learning
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