3 accepted papers
Underwater images suffer due to light scattering and color absorption, which is challenging for marine researchers, underwater heritage miners, and aquatic robotics. Due to the unavailability of ample real-world underwater ground truth images, it becomes more challenging to frame a deep learning-bas…
Tensor decompositions are powerful tools for dimensionality reduction and feature interpretation of multidimensional data such as signals. Existing tensor decomposition objectives (e.g., Frobenius norm) are designed for fitting raw data under statistical assumptions, which may not align with downstr…
Traditionally, federated learning (FL) aims to train a single global model while collaboratively using multiple clients and a server. Two natural challenges that FL algorithms face are heterogeneity in data across clients and collaboration of clients with diverse resources. In this work, we introduc…