no code implementations • 31 May 2023 • Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro
To address IDN, Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples.
no code implementations • 20 Mar 2023 • Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects.
1 code implementation • 2 Sep 2022 • Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them.
Ranked #1 on Learning with noisy labels on CIFAR-100
1 code implementation • 4 Dec 2020 • Parshwa Shah, Arpit Garg, Vandit Gajjar
Instead of using an image query, in this paper, we study the problem of person retrieval in video surveillance with a semantic description.
no code implementations • 9 Oct 2019 • Arpit Garg, Yazied A. Hasan, Adam Yañez, Lydia Tapia
When compared to a state-of-art algorithm for obstacle avoidance, our solution with a single escort increases navigation success up to 31%.