1 code implementation • 12 Feb 2024 • Tanmoy Dam, Sanjay Bhargav Dharavath, Sameer Alam, Nimrod Lilith, Supriyo Chakraborty, Mir Feroskhan
Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems.
1 code implementation • 9 Jan 2024 • Supriyo Chakraborty, Aurobinda Routray, Sanjay Bhargav Dharavath, Tanmoy Dam
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.
no code implementations • 12 Oct 2023 • Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar, Vu N Duong
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.
no code implementations • 21 Apr 2023 • Md Rasel Sarkar, Sreenatha G. Anavatti, Tanmoy Dam, Mahardhika Pratama, Berlian Al Kindhi
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.
1 code implementation • 21 Apr 2023 • Gao Yu Lee, Tanmoy Dam, Md Meftahul Ferdaus, Daniel Puiu Poenar, Vu N. Duong
Incorporating deep learning (DL) classification models into unmanned aerial vehicles (UAVs) can significantly augment search-and-rescue operations and disaster management efforts.
1 code implementation • 7 Sep 2022 • Harshita Boonlia, Tanmoy Dam, Md Meftahul Ferdaus, Sreenatha G. Anavatti, Ankan Mullick
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.
1 code implementation • 4 Sep 2022 • Tanmoy Dam, Mahardhika Pratama, Md Meftahul Ferdaus, Sreenatha Anavatti, Hussein Abbas
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.
no code implementations • 4 Sep 2022 • Tanmoy Dam, Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti, Senthilnath Jayavelu, Hussein A. Abbass
Many real-world classification problems have imbalanced frequency of class labels; a well-known issue known as the "class imbalance" problem.
no code implementations • 7 Nov 2021 • Tanmoy Dam, Nidhi Swami, Sreenatha G. Anavatti, Hussein A. Abbass
This paper presents a novel multi-fake evolutionary generative adversarial network(MFEGAN) for handling imbalance hyperspectral image classification.
no code implementations • 22 Aug 2021 • Subhrasankha Dey, Tanmoy Dam
We present comparative performance measures of GK algorithms with two other clustering algorithms: (i) Fuzzy C-Means (FCM), and (ii)Subtractive Clustering (SC).
no code implementations • 20 Aug 2021 • Tanmoy Dam, Md Meftahul Ferdaus, Sreenatha G. Anavatti, Senthilnath Jayavelu, Hussein A. Abbass
Rather than adversarial minority oversampling, we propose an adversarial oversampling (AO) and a data-space oversampling (DO) approach.
no code implementations • 27 Jul 2021 • Tanmoy Dam, Sreenatha G. Anavatti, Hussein A. Abbass
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.