JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
Loss Functions for Neural Networks for Image Processing
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
Neural networks are becoming central in several areas of computer vision and image processing and different architectures have been proposed to solve specific problems. The impact of the loss layer of neural networks, however, has not received much attention in the context of image processing: the default and virtually only choice is L2. In this paper, we bring attention to alternative choices for image restoration. In particular, we show the importance of perceptually-motivated losses when the resulting image is to be evaluated by a human observer. We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.
verdicts
UNVERDICTED 2representative citing papers
HiFi-Mamba uses stacked W-Laplacian spectral decoupling and unidirectional HiFi-Mamba blocks to improve high-frequency detail preservation and efficiency over prior Mamba, CNN, and Transformer models for MRI reconstruction.
citing papers explorer
-
Let EEG Models Learn EEG
JET is a conditional flow matching framework that generates EEG as continuous raw sequences with added constraints for spectral and temporal properties, achieving over 40% lower TS-FID than prior discrete denoising methods on three benchmarks.
-
HiFi-Mamba: Dual-Stream W-Laplacian Enhanced Mamba for High-Fidelity MRI Reconstruction
HiFi-Mamba uses stacked W-Laplacian spectral decoupling and unidirectional HiFi-Mamba blocks to improve high-frequency detail preservation and efficiency over prior Mamba, CNN, and Transformer models for MRI reconstruction.