Search Results for author: Deepti R. Bathula

Found 7 papers, 1 papers with code

Leveraging Different Learning Styles for Improved Knowledge Distillation

no code implementations6 Dec 2022 Usma Niyaz, Deepti R. Bathula

Unlike conventional techniques that share the same type of knowledge with all networks, we propose to train individual networks with different forms of information to enhance the learning process.

Knowledge Distillation Model Compression

IPNET:Influential Prototypical Networks for Few Shot Learning

no code implementations19 Aug 2022 Ranjana Roy Chowdhury, Deepti R. Bathula

Further, the influence factor of a sample is measured using MMD based on the shift in the distribution in the absence of that sample.

Few-Shot Learning

Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study

no code implementations1 Nov 2021 Ranjana Roy Chowdhury, Deepti R. Bathula

Conventional PN attributes equal importance to all samples and generates prototypes by simply averaging the support sample embeddings belonging to each class.

Domain Adaptation Few-Shot Learning

Towards Reducing Aleatoric Uncertainty for Medical Imaging Tasks

no code implementations21 Oct 2021 Abhishek Singh Sambyal, Narayanan C. Krishnan, Deepti R. Bathula

The proposed method was evaluated on a benchmark medical imaging dataset with image reconstruction as the self-supervised task and segmentation as the image analysis task.

Data Augmentation Decision Making Under Uncertainty +4

Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression

no code implementations21 Oct 2021 Usma Niyaz, Deepti R. Bathula

Knowledge distillation (KD) is an effective model compression technique where a compact student network is taught to mimic the behavior of a complex and highly trained teacher network.

Knowledge Distillation Model Compression +3

EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification

1 code implementation8 Jul 2021 Subhranil Bagchi, Deepti R. Bathula

Different categories of visual stimuli activate different responses in the human brain.

Classification EEG

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