Hierarchical Online-Scheduling for Energy-Efficient Split Inference with Progressive Transmission

A recent study has introduced Concept-Based Diversity (CBD), a highly efficient metric for image inputs that utilizes Vision-Language Models (VLMs) to enhance the performance of Deep Neural Networks (DNNs) through improved input selection. This approach addresses the computational intensity and scalability issues associated with traditional diversity-based selection methods.

The recent introduction of NOVAK, a unified adaptive optimizer for deep neural networks, combines several advanced techniques including adaptive moment estimation and lookahead synchronization, aiming to enhance the performance and efficiency of neural network training.

A recent study titled 'When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning' proposes a universal training-free method for model calibration, cascading, and data cleaning, enhancing models' ability to recognize their limitations. The research highlights that higher confidence correlates with higher accuracy and that models calibrated on validation sets maintain their calibration on test sets.

A new model called Inception Generative Adversarial Network (IGAN) has been introduced, addressing the challenges of high-quality image synthesis and training stability in Generative Adversarial Networks (GANs). The IGAN model utilizes deeper inception-inspired and dilated convolutions, achieving significant improvements in image fidelity with a Frechet Inception Distance (FID) of 13.12 and 15.08 on the CUB-200 and ImageNet datasets, respectively.