Jathushan Rajasegaran

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Meta-learning the Learning Trends Shared Across Tasks

Meta-learning stands for 'learning to learn' such that generalization to...
10/19/2020 ∙ by Jathushan Rajasegaran, et al. ∙ 0 ∙ share

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Self-supervised Knowledge Distillation for Few-shot Learning

Real-world contains an overwhelmingly large number of object classes, le...
06/17/2020 ∙ by Jathushan Rajasegaran, et al. ∙ 77 ∙ share

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A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store

Counterfeit apps impersonate existing popular apps in attempts to misgui...
06/02/2020 ∙ by Naveen Karunanayake, et al. ∙ 0 ∙ share

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iTAML: An Incremental Task-Agnostic Meta-learning Approach

Humans can continuously learn new knowledge as their experience grows. I...
03/25/2020 ∙ by Jathushan Rajasegaran, et al. ∙ 4 ∙ share

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Incremental Object Detection via Meta-Learning

In a real-world setting, object instances from new classes may be contin...
03/17/2020 ∙ by K J Joseph, et al. ∙ 11 ∙ share

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TimeCaps: Capturing Time Series Data with Capsule Networks

Capsule networks excel in understanding spatial relationships in 2D data...
11/26/2019 ∙ by Hirunima Jayasekara, et al. ∙ 0 ∙ share

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Random Path Selection for Incremental Learning

Incremental life-long learning is a main challenge towards the long-stan...
06/03/2019 ∙ by Jathushan Rajasegaran, et al. ∙ 0 ∙ share

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DeepCaps: Going Deeper with Capsule Networks

Capsule Network is a promising concept in deep learning, yet its true po...
04/21/2019 ∙ by Jathushan Rajasegaran, et al. ∙ 0 ∙ share

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TextCaps : Handwritten Character Recognition with Very Small Datasets

Many localized languages struggle to reap the benefits of recent advance...
04/17/2019 ∙ by Vinoj Jayasundara, et al. ∙ 0 ∙ share

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Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos

Activity recognition in videos in a deep-learning setting---or otherwise...
10/16/2018 ∙ by Sameera Ramasinghe, et al. ∙ 0 ∙ share

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A Neural Embeddings Approach for Detecting Mobile Counterfeit Apps

Counterfeit apps impersonate existing popular apps in attempts to misgui...
04/26/2018 ∙ by Jathushan Rajasegaran, et al. ∙ 0 ∙ share

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Jathushan Rajasegaran

Featured Co-authors

Ling Shao

Mubarak Shah

Fahad Shahbaz Khan

Salman Khan

Munawar Hayat

Suranga Seneviratne

Ranga Rodrigo

Sameera Ramasinghe

Vinoj Jayasundara

Guillaume Jourjon

K J Joseph

Meta-learning the Learning Trends Shared Across Tasks

Self-supervised Knowledge Distillation for Few-shot Learning

A Multi-modal Neural Embeddings Approach for Detecting Mobile Counterfeit Apps: A Case Study on Google Play Store

iTAML: An Incremental Task-Agnostic Meta-learning Approach

Incremental Object Detection via Meta-Learning

TimeCaps: Capturing Time Series Data with Capsule Networks

Random Path Selection for Incremental Learning

DeepCaps: Going Deeper with Capsule Networks

TextCaps : Handwritten Character Recognition with Very Small Datasets

Combined Static and Motion Features for Deep-Networks Based Activity Recognition in Videos

A Neural Embeddings Approach for Detecting Mobile Counterfeit Apps