75 citations · 654 across the 44 of their papers we have counts for
9 papers · 1 filter
MF-NeRF: Memory Efficient NeRF with Mixed-Feature Hash Table
Yongjae Lee, Li Yang, Deliang Fan
Neural radiance field (NeRF) has shown remarkable performance in generating photo-realistic novel views. Among recent NeRF related research, the approaches that involve the utiliza…
:Dynamic Additive Attention Adaption for Memory-EfficientOn-Device Multi-Domain Learning
Li Yang, Adnan Siraj Rakin, Deliang Fan
Nowadays, one practical limitation of deep neural network (DNN) is its high degree of specialization to a single task or domain (e.g., one visual domain). It motivates researchers…
A Progressive Sub-Network Searching Framework for Dynamic Inference
Li Yang, Zhezhi He, Yu Cao +1
Many techniques have been developed, such as model compression, to make Deep Neural Networks (DNNs) inference more efficiently. Nevertheless, DNNs still lack excellent run-time dyn…
KSM: Fast Multiple Task Adaption via Kernel-wise Soft Mask Learning
Li Yang, Zhezhi He, Junshan Zhang +1
Deep Neural Networks (DNN) could forget the knowledge about earlier tasks when learning new tasks, and this is known as \textit{catastrophic forgetting}. While recent continual lea…
Defending Against Adversarial Attacks Using Random Forests
Yifan Ding, Liqiang Wang, Huan Zhang +3
As deep neural networks (DNNs) have become increasingly important and popular, the robustness of DNNs is the key to the safety of both the Internet and the physical world. Unfortun…
Robust Sparse Regularization: Simultaneously Optimizing Neural Network Robustness and Compactness
Adnan Siraj Rakin, Zhezhi He, Li Yang +3
Deep Neural Network (DNN) trained by the gradient descent method is known to be vulnerable to maliciously perturbed adversarial input, aka. adversarial attack. As one of the counte…
Bit-Flip Attack: Crushing Neural Network with Progressive Bit Search
Adnan Siraj Rakin, Zhezhi He, Deliang Fan
Several important security issues of Deep Neural Network (DNN) have been raised recently associated with different applications and components. The most widely investigated securit…
Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy
Zhezhi He, Boqing Gong, Deliang Fan
Deep convolution neural network has achieved great success in many artificial intelligence applications. However, its enormous model size and massive computation cost have become t…
A Semi-Supervised Two-Stage Approach to Learning from Noisy Labels
Yifan Ding, Liqiang Wang, Deliang Fan +1
The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dat…