Improving Reliability of Sparse Deep Neural Networks for Real-World Applications

Sparse training exacerbates the unreliability of deep neural networks in detecting unknown out-of-distribution data. To address this, we propose a new unknown-aware sparse training method that leverages unknown information to guide weight space exploration and mitigate confusion between known and unknown, improving the real-world reliability of sparse deep neural networks.