arXiv subjects

Jayaprakash Katual

Publications and source records attributed to Jayaprakash Katual.

2 recordsLinked to original sources

arXiv2026

Tunable Domain Adaptation Using Unfolding

Machine learning models often struggle to generalize across domains with varying data distributions, such as differing noise levels, leading to degraded performance. Traditional strategies like personalized training, which trains separate models per domain, and joint training, which uses a single model for all domains, have significant limitations in flexibility and effectiveness. To address this, we propose two novel domain adaptation methods for regression tasks based on interpretable unrolled networks--deep architectures inspired by iterative optimization algorithms. These models leverage the functional dependence of select tunable parameters on domain variables, enabling controlled adaptation during inference. Our methods include Parametric Tunable-Domain Adaptation (P-TDA), which uses known domain parameters for dynamic tuning, and Data-Driven Tunable-Domain Adaptation (DD-TDA), which infers domain adaptation directly from input data. We validate our approach on compressed sensing problems involving noise-adaptive sparse signal recovery, domain-adaptive gain calibration, and domain-adaptive phase retrieval, demonstrating improved or comparable performance to domain-specific models while surpassing joint training baselines. This work highlights the potential of unrolled networks for effective, interpretable domain adaptation in regression settings.

cs.LG↗

arXiv2025

Dictionary-Based Deblurring for Unpaired Data

Effective image deblurring typically relies on large and fully paired datasets of blurred and corresponding sharp images. However, obtaining such accurately aligned data in the real world poses a number of difficulties, limiting the effectiveness and generalizability of existing deblurring methods. To address this scarcity of data dependency, we present a novel dictionary learning based deblurring approach for jointly estimating a structured blur matrix and a high resolution image dictionary. This framework enables robust image deblurring across different degrees of data supervision. Our method is thoroughly evaluated across three distinct experimental settings: (i) full supervision involving paired data with explicit correspondence, (ii) partial supervision employing unpaired data with implicit relationships, and (iii) unsupervised learning using non-correspondence data where direct pairings are absent. Extensive experimental validation, performed on synthetically blurred subsets of the CMU-Cornell iCoseg dataset and the real-world FocusPath dataset, consistently shows that the proposed framework has superior performance compared to conventional coupled dictionary learning approaches. The results validate that our approach provides an efficient and robust solution for image deblurring in data-constrained scenarios by enabling accurate blur modeling and adaptive dictionary representation with a notably smaller number of training samples.

eess.IV↗