28 citations · 98 across the 15 of their papers we have counts for
5 papers · 1 filter
Distill Gold from Massive Ores: Bi-level Data Pruning towards Efficient Dataset Distillation
Yue Xu, Yong-Lu Li, Kaitong Cui +4
Data-efficient learning has garnered significant attention, especially given the current trend of large multi-modal models. Recently, dataset distillation has become an effective a…
NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks
Wenkai Tan, Justus Renkhoff, Alvaro Velasquez +7
Deep Learning (DL) and Deep Neural Networks (DNNs) are widely used in various domains. However, adversarial attacks can easily mislead a neural network and lead to wrong decisions.…
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Qipeng Guo, Zhijing Jin, Ziyu Wang +5
Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched…
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
Bin Dai, Ziyu Wang, David Wipf
In narrow asymptotic settings Gaussian VAE models of continuous data have been shown to possess global optima aligned with ground-truth distributions. Even so, it is well known tha…
Deep Fried Convnets
Zichao Yang, Marcin Moczulski, Misha Denil +4
The fully connected layers of a deep convolutional neural network typically contain over 90% of the network parameters, and consume the majority of the memory required to store the…