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Found 16 papers, 6 papers with code
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
To ensure the fusion module maximally preserves the information from the source images, enabling the reconstruction of the source images from the fused image, we adopt a meta-learning strategy to train the loss proposal module using reconstruction loss.
CBA: Improving Online Continual Learning via Continual Bias Adaptor
Online continual learning (CL) aims to learn new knowledge and consolidate previously learned knowledge from non-stationary data streams.
Virtual impactor-based label-free bio-aerosol detection using holography and deep learning
To address this need, we present a mobile and cost-effective label-free bio-aerosol sensor that takes holographic images of flowing particulate matter concentrated by a virtual impactor, which selectively slows down and guides particles larger than ~6 microns to fly through an imaging window.
Imbalanced Semi-supervised Learning with Bias Adaptive Classifier
The core idea is to automatically assimilate the training bias caused by class imbalance via the bias adaptive classifier, which is composed of a novel bias attractor and the original linear classifier.
Learning to generate imaginary tasks for improving generalization in meta-learning
The success of meta-learning on existing benchmarks is predicated on the assumption that the distribution of meta-training tasks covers meta-testing tasks.
Dynamic imaging and characterization of volatile aerosols in e-cigarette emissions using deep learning-based holographic microscopy
To evaluate the effects of e-liquid composition on aerosol dynamics, we measured the volatility of the particles generated by flavorless, nicotine-free e-liquids with various PG/VG volumetric ratios, revealing a negative correlation between the particles' volatility and the volumetric ratio of VG in the e-liquid.
Deep learning-based virtual refocusing of images using an engineered point-spread function
We present a virtual image refocusing method over an extended depth of field (DOF) enabled by cascaded neural networks and a double-helix point-spread function (DH-PSF).
Learning to Purify Noisy Labels via Meta Soft Label Corrector
By viewing the label correction procedure as a meta-process and using a meta-learner to automatically correct labels, we could adaptively obtain rectified soft labels iteratively according to current training problems without manually preset hyper-parameters.
Structural Residual Learning for Single Image Rain Removal
Such a structural residual setting guarantees the rain layer extracted by the network finely comply with the prior knowledge of general rain streaks, and thus regulates sound rain shapes capable of being well extracted from rainy images in both training and predicting stages.
A Survey on Rain Removal from Video and Single Image
The investigations on rain removal from video or a single image has thus been attracting much research attention in the field of computer vision and pattern recognition, and various methods have been proposed against this task in the recent years.
Deep learning-based color holographic microscopy
We report a framework based on a generative adversarial network (GAN) that performs high-fidelity color image reconstruction using a single hologram of a sample that is illuminated simultaneously by light at three different wavelengths.
Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning
Three-dimensional (3D) fluorescence microscopy in general requires axial scanning to capture images of a sample at different planes.
Resolution enhancement in scanning electron microscopy using deep learning
We report resolution enhancement in scanning electron microscopy (SEM) images using a generative adversarial network.
Cross-modality deep learning brings bright-field microscopy contrast to holography
Deep learning brings bright-field microscopy contrast to holographic images of a sample volume, bridging the volumetric imaging capability of holography with the speckle- and artifact-free image contrast of bright-field incoherent microscopy.
Extended depth-of-field in holographic image reconstruction using deep learning based auto-focusing and phase-recovery
Holography encodes the three dimensional (3D) information of a sample in the form of an intensity-only recording.
A Practical Algorithm for Topic Modeling with Provable Guarantees
Topic models provide a useful method for dimensionality reduction and exploratory data analysis in large text corpora.
