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Found 11 papers, 8 papers with code
Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning
In this paper, we look at cross-domain few-shot classification which presents the challenging task of learning new classes in previously unseen domains with few labelled examples.
Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data
In this work, we propose two deep-learning-based regional forecasting methods that can effectively leverage both types of time series (aggregated and individual) with weather data in a region.
When To Grow? A Fitting Risk-Aware Policy for Layer Growing in Deep Neural Networks
Neural growth is the process of growing a small neural network to a large network and has been utilized to accelerate the training of deep neural networks.
GINN-LP: A Growing Interpretable Neural Network for Discovering Multivariate Laurent Polynomial Equations
In this work, we propose GINN-LP, an interpretable neural network to discover the form and coefficients of the underlying equation of a dataset, when the equation is assumed to take the form of a multivariate Laurent Polynomial.
NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning
However, due to the lack of old data, NECIL methods struggle to discriminate between old and new classes causing their feature representations to overlap.
Incremental Learning
Non-exemplar-based Class Incremental Learning
+1
Undercover Deepfakes: Detecting Fake Segments in Videos
This paradigm has been under-explored by the current deepfake detection methods in the academic literature.
NAPA-VQ: Neighborhood-Aware Prototype Augmentation with Vector Quantization for Continual Learning
However, due to the lack of old data, NECIL methods struggle to discriminate between old and new classes causing their feature representations to overlap.
Incremental Learning
Non-exemplar-based Class Incremental Learning
+1
Multi-Resolution, Multi-Horizon Distributed Solar PV Power Forecasting with Forecast Combinations
We propose a forecast combination approach based on particle swarm optimization (PSO) that will enable a forecaster to produce accurate forecasts for the task at hand by weighting the forecasts produced by individual models.
A machine learning accelerated inverse design of underwater acoustic polyurethane coatings with cylindrical voids
Here, we report the development of a detailed "Materials Informatics" framework for the design of acoustic coatings for underwater sound attenuation through integrating Machine Learning (ML) and statistical optimization algorithms with a Finite Element Model (FEM).
Self Organizing Nebulous Growths for Robust and Incremental Data Visualization
In addition, SONG is capable of handling new data increments, no matter whether they are similar or heterogeneous to the already observed data distribution.
Improving MMD-GAN Training with Repulsive Loss Function
To address this issue, we propose a repulsive loss function to actively learn the difference among the real data by simply rearranging the terms in MMD.
Ranked #19 on
Image Generation
on STL-10
