11 citations · 36 across the 17 of their papers we have counts for
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cs.LG2023
GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks
Stone Yun, Alexander Wong
Graph Hypernetworks (GHN) can predict the parameters of varying unseen CNN architectures with surprisingly good accuracy at a fraction of the cost of iterative optimization. Follow…
cs.LG2023
Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability
E. Zhixuan Zeng, Hayden Gunraj, Sheldon Fernandez +1
Explainability plays a crucial role in providing a more comprehensive understanding of deep learning models' behaviour. This allows for thorough validation of the model's performan…
cs.CV2023★ 4 cited
DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection
Hossein Aboutalebi, Dayou Mao, Rongqi Fan +3
The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from…