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Found 9 papers, 1 papers with code
Enhancing Wind Power Forecast Precision via Multi-head Attention Transformer: An Investigation on Single-step and Multi-step Forecasting
The proposed model is evaluated for single-step and multi-step WPF, and compared with gated recurrent unit (GRU) and long short-term memory (LSTM) models on a wind power dataset.
Lightweight Monocular Depth Estimation with an Edge Guided Network
In order to aggregate the context information and edge attention features, we design a transformer-based feature aggregation module (TRFA).
Improving Self-supervised Learning for Out-of-distribution Task via Auxiliary Classifier
Observing a strong relationship between rotation prediction (self-supervised) accuracy and semantic classification accuracy on OOD tasks, we introduce an additional auxiliary classification head in our multi-task network along with semantic classification and rotation prediction head.
Latent Preserving Generative Adversarial Network for Imbalance classification
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem.
Multi-Fake Evolutionary Generative Adversarial Networks for Imbalance Hyperspectral Image Classification
This paper presents a novel multi-fake evolutionary generative adversarial network(MFEGAN) for handling imbalance hyperspectral image classification.
Does Adversarial Oversampling Help us?
Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach.
Improving ClusterGAN Using Self-Augmented Information Maximization of Disentangling Latent Spaces
Since the real conditional distribution of data is ignored, the clustering inference network can only achieve inferior clustering performance by considering only uniform prior based generative samples.
Review of Applications of Generalized Regression Neural Networks in Identification and Control of Dynamic Systems
This paper depicts a brief revision of Generalized Regression Neural Networks (GRNN) applications in system identification and control of dynamic systems.
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
Data stream has been the underlying challenge in the age of big data because it calls for real-time data processing with the absence of a retraining process and/or an iterative learning approach.
