Search Results for author:
Found 8 papers, 5 papers with code
Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?
Self-supervised learning (SSL) has emerged as a promising solution for addressing the challenge of limited labeled data in deep neural networks (DNNs), offering scalability potential.
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training
Adversarial training improves the robustness of neural networks against adversarial attacks, albeit at the expense of the trade-off between standard and robust generalization.
Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning
Artificial neural networks (ANNs) exhibit a narrow scope of expertise on stationary independent data.
LSFSL: Leveraging Shape Information in Few-shot Learning
Few-shot learning (FSL) techniques seek to learn the underlying patterns in data using fewer samples, analogous to how humans learn from limited experience.
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey
Our extensive empirical study can act as a guideline for the industrial community to make an informed choice on the existing networks.
InBiaseD: Inductive Bias Distillation to Improve Generalization and Robustness through Shape-awareness
Humans rely less on spurious correlations and trivial cues, such as texture, compared to deep neural networks which lead to better generalization and robustness.
Does Thermal data make the detection systems more reliable?
To overcome these challenges, we explore the idea of leveraging a different data modality that is disparate yet complementary to the visual data.
AI Driven Road Maintenance Inspection
Road infrastructure maintenance inspection is typically a labour-intensive and critical task to ensure the safety of all the road users.
