4 papers
When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide
Hang-Cheng Dong, Yuhao Jiang, Yibo Jiao +5
The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely hampered by their lack of reli…
Memory-Efficient Transfer Learning with Fading Side Networks via Masked Dual Path Distillation
Yutong Zhang, Jiaxin Chen, Honglin Chen +5
Memory-efficient transfer learning (METL) approaches have recently achieved promising performance in adapting pre-trained models to downstream tasks. They avoid applying gradient b…
MaskVD: Region Masking for Efficient Video Object Detection
Sreetama Sarkar, Gourav Datta, Souvik Kundu +3
Video tasks are compute-heavy and thus pose a challenge when deploying in real-time applications, particularly for tasks that require state-of-the-art Vision Transformers (ViTs). S…
Block Selective Reprogramming for On-device Training of Vision Transformers
Sreetama Sarkar, Souvik Kundu, Kai Zheng +1
The ubiquity of vision transformers (ViTs) for various edge applications, including personalized learning, has created the demand for on-device fine-tuning. However, training with…