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Found 12 papers, 5 papers with code
AYDIV: Adaptable Yielding 3D Object Detection via Integrated Contextual Vision Transformer
Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems.
OrthoSeisnet: Seismic Inversion through Orthogonal Multi-scale Frequency Domain U-Net for Geophysical Exploration
However, the detection of sparse thin layers within seismic datasets presents a significant challenge due to the ill-posed nature and poor non-linearity of the problem.
Unlocking the capabilities of explainable fewshot learning in remote sensing
While previous research has evaluated the effectiveness of fewshot learning methods on satellite based datasets, little attention has been paid to exploring the applications of these methods to datasets obtained from UAVs, which are increasingly used in remote sensing studies.
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.
WATT-EffNet: A Lightweight and Accurate Model for Classifying Aerial Disaster Images
Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts.
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.
Scalable Adversarial Online Continual Learning
Adversarial continual learning is effective for continual learning problems because of the presence of feature alignment process generating task-invariant features having low susceptibility to the catastrophic forgetting problem.
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.
Rainfall-runoff prediction using a Gustafson-Kessel clustering based Takagi-Sugeno Fuzzy model
We present comparative performance measures of GK algorithms with two other clustering algorithms: (i) Fuzzy C-Means (FCM), and (ii)Subtractive Clustering (SC).
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.
