AutoMCU uses feasibility-first LLM multi-agent coordination to automate MCU-constrained neural network design, delivering competitive accuracy on CIFAR-10/100 in 1-2 hours versus hundreds of GPU hours for prior HW-NAS methods.
Deep residual learning for image recognition
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
LatRef-Diff replaces semantic directions in diffusion models with latent and reference-guided style codes, uses a hierarchical style modulation module, and applies forward-backward consistency training to achieve state-of-the-art facial attribute editing and style manipulation on CelebA-HQ.
A new framework learns low-dimensional subspaces from nominal samples and reconstructs target deep embeddings via self-expressive linear combinations to localize anomalies, claiming SOTA on three benchmarks.
citing papers explorer
-
AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems
AutoMCU uses feasibility-first LLM multi-agent coordination to automate MCU-constrained neural network design, delivering competitive accuracy on CIFAR-10/100 in 1-2 hours versus hundreds of GPU hours for prior HW-NAS methods.
-
LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation
LatRef-Diff replaces semantic directions in diffusion models with latent and reference-guided style codes, uses a hierarchical style modulation module, and applies forward-backward consistency training to achieve state-of-the-art facial attribute editing and style manipulation on CelebA-HQ.
-
Subspace-Guided Feature Reconstruction for Unsupervised Anomaly Localization
A new framework learns low-dimensional subspaces from nominal samples and reconstructs target deep embeddings via self-expressive linear combinations to localize anomalies, claiming SOTA on three benchmarks.