9 citations · 24 across the 14 of their papers we have counts for
14 papers
The Surprising Effectiveness of Canonical Knowledge Distillation for Semantic Segmentation
Muhammad Ali, Kevin Alexander Laube, Madan Ravi Ganesh +3
Recent knowledge distillation (KD) methods for semantic segmentation introduce increasingly complex hand-crafted objectives, yet are typically evaluated under fixed iteration sched…
FedRPCA: Enhancing Federated LoRA Aggregation Using Robust PCA
Divyansh Jhunjhunwala, Arian Raje, Madan Ravi Ganesh +6
LoRA has emerged as one of the most promising fine-tuning techniques, especially for federated learning (FL), since it significantly reduces communication and computation costs at…
HyperCLIP: Adapting Vision-Language models with Hypernetworks
Victor Akinwande, Mohammad Sadegh Norouzzadeh, Devin Willmott +3
Self-supervised vision-language models trained with contrastive objectives form the basis of current state-of-the-art methods in AI vision tasks. The success of these models is a d…
Leveraging Foundation Models to Improve Lightweight Clients in Federated Learning
Xidong Wu, Wan-Yi Lin, Devin Willmott +4
Federated Learning (FL) is a distributed training paradigm that enables clients scattered across the world to cooperatively learn a global model without divulging confidential data…
Text-driven Prompt Generation for Vision-Language Models in Federated Learning
Chen Qiu, Xingyu Li, Chaithanya Kumar Mummadi +4
Prompt learning for vision-language models, e.g., CoOp, has shown great success in adapting CLIP to different downstream tasks, making it a promising solution for federated learnin…
Q-TART: Quickly Training for Adversarial Robustness and in-Transferability
Madan Ravi Ganesh, Salimeh Yasaei Sekeh, Jason J. Corso
Raw deep neural network (DNN) performance is not enough; in real-world settings, computational load, training efficiency and adversarial security are just as or even more important…