1 citations · 2 across the 7 of their papers we have counts for
7 papers
Few-Shot Domain Incremental Learning via Continual Vision-Language Consolidation
Naeem Paeedeh, Mahardhika Pratama, Wolfgang Mayer +3
Existing domain-incremental learning (DIL) strategies call for massive amounts of data to adapt to new domains and suffer from the overfitting problem in the case of data scarcity.…
Cross-Domain Few-Shot Learning for Hyperspectral Image Classification Based on Mixup Foundation Model
Naeem Paeedeh, Mahardhika Pratama, Ary Shiddiqi +3
Although cross-domain few-shot learning (CDFSL) for hyper-spectral image (HSI) classification has attracted significant research interest, existing works often rely on an unrealist…
Continual Knowledge Consolidation LORA for Domain Incremental Learning
Naeem Paeedeh, Mahardhika Pratama, Weiping Ding +4
Domain Incremental Learning (DIL) is a sub-branch of continual learning that aims to address the never-ending arrival of new domains without catastrophic forgetting. Despite the ad…
Cross-Domain Few-Shot Learning with Coalescent Projections and Latent Space Reservation
Naeem Paeedeh, Mahardhika Pratama, Imam Mustafa Kamal +3
Despite the progress in cross-domain few-shot learning, a model pre-trained with DINO combined with a prototypical classifier outperforms the latest SOTA methods. A crucial limitat…
Few-Shot Class Incremental Learning via Robust Transformer Approach
Naeem Paeedeh, Mahardhika Pratama, Sunu Wibirama +3
Few-Shot Class-Incremental Learning presents an extension of the Class Incremental Learning problem where a model is faced with the problem of data scarcity while addressing the ca…
Cross-Domain Few-Shot Learning via Adaptive Transformer Networks
Naeem Paeedeh, Mahardhika Pratama, Muhammad Anwar Ma'sum +3
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications. This paper proposes an adaptive trans…
Improving the Backpropagation Algorithm with Consequentialism Weight Updates over Mini-Batches
Naeem Paeedeh, Kamaledin Ghiasi-Shirazi
Many attempts took place to improve the adaptive filters that can also be useful to improve backpropagation (BP). Normalized least mean squares (NLMS) is one of the most successful…