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Found 7 papers, 1 papers with code
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts
Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data.
On the Importance of Spatial Relations for Few-shot Action Recognition
We are thus motivated to investigate the importance of spatial relations and propose a more accurate few-shot action recognition method that leverages both spatial and temporal information.
Sampling to Distill: Knowledge Transfer from Open-World Data
Then, we introduce a low-noise representation to alleviate the domain shifts and build a structured relationship of multiple data examples to exploit data knowledge.
Out of Thin Air: Exploring Data-Free Adversarial Robustness Distillation
Adversarial Robustness Distillation (ARD) is a promising task to solve the issue of limited adversarial robustness of small capacity models while optimizing the expensive computational costs of Adversarial Training (AT).
Explicit and Implicit Knowledge Distillation via Unlabeled Data
Data-free knowledge distillation is a challenging model lightweight task for scenarios in which the original dataset is not available.
Adversarial Contrastive Distillation with Adaptive Denoising
To this end, we propose a novel structured ARD method called Contrastive Relationship DeNoise Distillation (CRDND).
An Experimental-based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging
Underwater images play a key role in ocean exploration, but often suffer from severe quality degradation due to light absorption and scattering in water medium.
