On the ability of CNNs to extract color invariant intensity based features for image classification

Convolutional neural networks (CNNs) have demonstrated remarkable success in vision-related tasks. However, their susceptibility to failing when inputs deviate from the training distribution is well-documented. Recent studies suggest that CNNs exhibit a bias toward texture instead of object shape in image classification tasks, and that background information may affect predictions. This paper investigates the ability of CNNs to adapt to different color distributions in an image while maintaining context and background. The results of our experiments on modified MNIST and FashionMNIST data demonstrate that changes in color can substantially affect classification accuracy. The paper explores the effects of various regularization techniques on generalization error across datasets and proposes a minor architectural modification utilizing the dropout regularization in a novel way that enhances model reliance on color-invariant intensity-based features for improved classification accuracy. Overall, this work contributes to ongoing efforts to understand the limitations and challenges of CNNs in image classification tasks and offers potential solutions to enhance their performance

Room temperature ferromagnetism and white light emissive CdS:Cr nanoparticles synthesized by chemical co-precipitation method

Undoped and Cr (3% and 5%) doped CdS nanoparticles were synthesized by chemical co-precipitation method. The synthesized nanocrystalline particles are characterized by energy dispersive X-ray analysis (EDAX), scanning electron microscope (SEM), X-ray Diffraction (XRD), transmission electron microscopy (TEM), diffuse reflectance spectroscopy (DRS), photoluminescence (PL), Electron paramagnetic resonance (EPR), vibrating sample magnetometer (VSM) and Raman spectroscopy. XRD studies indicate that Cr doping in host CdS result a structural change from Cubic phase to mixed (cubic + hexagonal) phase. Due to quantum confinement effect, widening of the band gap is observed for undoped and Cr doped CdS nanoparticles compared to bulk CdS. The average particle size calculated from band gap values is in good agreement with the TEM study calculation and it is around 4-5 nm. A strong violet emission band consisting of two emission peaks is observed for undoped CdS nanoparticles, whereas for CdS:Cr nanoparticles, a broad emission band ranging from 420 nm to 730 nm with a maximum at similar to 587 nm is observed. The broad emission band is due to the overlapped emissions from variety of defects. EPR spectra of CdS:Cr samples reveal resonance signal at g = 2.143 corresponding to interacting Cr3+ ions. VSM studies indicate that the diamagnetic CdS nanoparticles are transform to ferromagnetic for 3% Cr3+ doping and the ferromagnetic nature is diminished with increasing the doping concentration to 5%. (C) 2015 Elsevier B.V. All rights reserved