7 papers
Learned Multimodal Compression for Autonomous Driving
Hadi Hadizadeh, Ivan V. BajiÄ
Autonomous driving sensors generate an enormous amount of data. In this paper, we explore learned multimodal compression for autonomous driving, specifically targeted at 3D object…
Towards Task-Compatible Compressible Representations
Anderson de Andrade, Ivan BajiÄ
We identify an issue in multi-task learnable compression, in which a representation learned for one task does not positively contribute to the rate-distortion performance of a diff…
Learned Compression of Encoding Distributions
Mateen Ulhaq, Ivan V. BajiÄ
The entropy bottleneck introduced by Ballé et al. is a common component used in many learned compression models. It encodes a transformed latent representation using a static dist…
Optimizing Split Points for Error-Resilient SplitFed Learning
Chamani Shiranthika, Parvaneh Saeedi, Ivan V. BajiÄ
Recent advancements in decentralized learning, such as Federated Learning (FL), Split Learning (SL), and Split Federated Learning (SplitFed), have expanded the potentials of machin…
Mutual Information Analysis in Multimodal Learning Systems
Hadi Hadizadeh, S. Faegheh Yeganli, Bahador Rashidi +1
In recent years, there has been a significant increase in applications of multimodal signal processing and analysis, largely driven by the increased availability of multimodal data…
Compressive Feature Selection for Remote Visual Multi-Task Inference
Saeed Ranjbar Alvar, Ivan V. BajiÄ
Deep models produce a number of features in each internal layer. A key problem in applications such as feature compression for remote inference is determining how important each fe…