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Found 16 papers, 10 papers with code
Spatial Transcriptomics Dimensionality Reduction using Wavelet Bases
We illustrate the performance of our methods by spatial structure recovery and gene expression reconstruction in simulation.
Sampling Strategy for Fine-Tuning Segmentation Models to Crisis Area under Scarcity of Data
We have applied our method to a deep learning model for semantic segmentation, U-Net, in a remote sensing application of building detection - one of the core use cases of remote sensing in humanitarian applications.
Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization
Among current formulations, concepts defines them by as a direction in a learned representation space.
Source data selection for out-of-domain generalization
Models that perform out-of-domain generalization borrow knowledge from heterogeneous source data and apply it to a related but distinct target task.
Interactive Visualization and Representation Analysis Applied to Glacier Segmentation
Interpretability has attracted increasing attention in earth observation problems.
Interpretability of a Deep Learning Model in the Application of Cardiac MRI Segmentation with an ACDC Challenge Dataset
In previous studies, the base method is applied to the classification of cardiac disease and provides clinically meaningful explanations for the predictions of a black-box deep learning classifier.
Conditional Networks
In this work we tackle the problem of out-of-distribution generalization through conditional computation.
FloW: A Dataset and Benchmark for Floating Waste Detection in Inland Waters
To promote the practical application for autonomous floating wastes cleaning, we present FloW, the first dataset for floating waste detection in inland water areas.
Machine Learning for Glacier Monitoring in the Hindu Kush Himalaya
Glacier mapping is key to ecological monitoring in the hkh region.
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
Nanoscale Microscopy Images Colorization Using Neural Networks
Microscopy images are powerful tools and widely used in the majority of research areas, such as biology, chemistry, physics and materials fields by various microscopies (scanning electron microscope (SEM), atomic force microscope (AFM) and the optical microscope, et al.).
Applying Knowledge Transfer for Water Body Segmentation in Peru
In this work, we present the application of convolutional neural networks for segmenting water bodies in satellite images.
Tackling Climate Change with Machine Learning
Climate change is one of the greatest challenges facing humanity, and we, as machine learning experts, may wonder how we can help.
Hierarchical Importance Weighted Autoencoders
We believe a joint proposal has the potential of reducing the number of redundant samples, and introduce a hierarchical structure to induce correlation.
Visualizing the Consequences of Climate Change Using Cycle-Consistent Adversarial Networks
We present a project that aims to generate images that depict accurate, vivid, and personalized outcomes of climate change using Cycle-Consistent Adversarial Networks (CycleGANs).
