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Device-First Feedback: Toward Mobile-Native LLM-Driven Neural Architecture Search
Saif U Din, Muhammad Ahsan Hussain, Radu Timofte +1
Deploying convolutional neural networks generated by large language models (LLMs) on real mobile hardware requires more than GPU validation accuracy: INT8 TensorFlow Lite export, d…
Similarity-Guided Curriculum Fine-Tuning of LLMs for Neural Architecture Synthesis
Anujaya Vijayakumar, Radu Timofte, Dmitry Ignatov
Introduce a MinHash-based similarity scheduling framework that constructs a progressive curriculum over neural architecture code for LLM-based neural architecture search (NAS). Usi…
Real Image Denoising with Knowledge Distillation for High-Performance Mobile NPUs
Faraz Kayani, Sarmad Kayani, Asad Ahmed +2
While deep-learning-based image restoration has achieved unprecedented fidelity, deployment on mobile Neural Processing Units (NPUs) remains bottlenecked by operator incompatibilit…
MobileAgeNet: Lightweight Facial Age Estimation for Mobile Deployment
Arun Kumar, Aswathy Baiju, Radu Timofte +1
Mobile deployment of facial age estimation requires models that balance predictive accuracy with low latency and compact size. In this work, we present MobileAgeNet, a lightweight…
Closed-Loop LLM Discovery of Non-Standard Channel Priors in Vision Models
Tolgay Atinc Uzun, Dmitry Ignatov, Radu Timofte
Channel-configuration search, the optimization of layer specifications such as channel widths in deep neural networks, presents a combinatorial challenge constrained by tensor-shap…
From Brute Force to Semantic Insight: Performance-Guided Data Transformation Design with LLMs
Usha Shrestha, Dmitry Ignatov, Radu Timofte
Large language models (LLMs) have achieved notable performance in code synthesis; however, data-aware augmentation remains a limiting factor, handled via heuristic design or brute-…
Enhancing LLM-Based Neural Network Generation: Few-Shot Prompting and Efficient Validation for Automated Architecture Design
Raghuvir Duvvuri, Chandini Vysyaraju, Avi Goyal +2
Automated neural network architecture design remains a significant challenge in computer vision. Task diversity and computational constraints require both effective architectures a…
A Retrieval-Augmented Generation Approach to Extracting Algorithmic Logic from Neural Networks
Waleed Khalid, Dmitry Ignatov, Radu Timofte
Reusing existing neural-network components is central to research efficiency, yet discovering, extracting, and validating such modules across thousands of open-source repositories…
AugmentGest: Can Random Data Cropping Augmentation Boost Gesture Recognition Performance?
Nada Aboudeshish, Dmitry Ignatov, Radu Timofte
Data augmentation is a crucial technique in deep learning, particularly for tasks with limited dataset diversity, such as skeleton-based datasets. This paper proposes a comprehensi…