Advancements in Efficient and Robust Deep Learning

The field of deep learning is moving towards developing more efficient and robust models, particularly in resource-constrained environments. Researchers are exploring innovative techniques such as ensemble learning, model compression, and fault protection to improve the performance and reliability of deep neural networks. Ensemble learning strategies are being used to enhance anomaly detection, accelerate deep ensemble learning, and balance robustness and efficiency in embedded systems. Additionally, novel methods are being proposed to protect deep neural networks from faults and errors, ensuring uninterrupted operation and maintaining accuracy. Noteworthy papers in this area include:

  • Efficient Ensemble Defense, which introduces a technique to diversify model compression for high adversarial robustness and resource efficiency.
  • Noisy Deep Ensemble, which proposes a novel method for accelerating deep ensemble learning via noise injection.
  • NAPER, which introduces a fault protection approach for real-time resource-constrained deep neural networks using ensemble learning.

Sources

Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models

Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection

Noisy Deep Ensemble: Accelerating Deep Ensemble Learning via Noise Injection

Enhanced Anomaly Detection for Capsule Endoscopy Using Ensemble Learning Strategies

NAPER: Fault Protection for Real-Time Resource-Constrained Deep Neural Networks

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