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Found 5 papers, 0 papers with code
Maximizing Discrimination Capability of Knowledge Distillation with Energy Function
To distill optimal knowledge by adjusting non-target class predictions, we apply a higher temperature to low energy samples to create smoother distributions and a lower temperature to high energy samples to achieve sharper distributions.
Cosine Similarity Knowledge Distillation for Individual Class Information Transfer
Furthermore, we propose a method called cosine similarity weighted temperature (CSWT) to improve the performance.
Robustness-Reinforced Knowledge Distillation with Correlation Distance and Network Pruning
The improvement in the performance of efficient and lightweight models (i. e., the student model) is achieved through knowledge distillation (KD), which involves transferring knowledge from more complex models (i. e., the teacher model).
NTIRE 2021 Challenge on Burst Super-Resolution: Methods and Results
This paper reviews the NTIRE2021 challenge on burst super-resolution.
Stochastic Quantized Activation: To prevent Overfitting in Fast Adversarial Training
Existing neural networks are vulnerable to "adversarial examples"---created by adding maliciously designed small perturbations in inputs to induce a misclassification by the networks.
