Ajinkya Tejankar

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University of California-Davis

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University of Maryland, Baltimore County

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∙ 06/16/2022

Backdoor Attacks on Vision Transformers

Vision Transformers (ViT) have recently demonstrated exemplary performan...

Akshayvarun Subramanya, et al. ∙

∙ 01/13/2022

SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation

Feature regression is a simple way to distill large neural network model...

K L Navaneet, et al. ∙

∙ 12/27/2021

A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision

Using natural language as a supervision for training visual recognition ...

Ajinkya Tejankar, et al. ∙

∙ 12/08/2021

Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning

We are interested in representation learning in self-supervised, supervi...

Ajinkya Tejankar, et al. ∙

∙ 10/19/2021

Constrained Mean Shift for Representation Learning

We are interested in representation learning from labeled or unlabeled d...

Ajinkya Tejankar, et al. ∙

∙ 05/21/2021

Backdoor Attacks on Self-Supervised Learning

Large-scale unlabeled data has allowed recent progress in self-supervise...

Aniruddha Saha, et al. ∙

∙ 05/15/2021

Mean Shift for Self-Supervised Learning

Most recent self-supervised learning (SSL) algorithms learn features by ...

Soroush Abbasi Koohpayegani, et al. ∙

∙ 12/16/2020

ISD: Self-Supervised Learning by Iterative Similarity Distillation

Recently, contrastive learning has achieved great results in self-superv...

Ajinkya Tejankar, et al. ∙

∙ 10/28/2020

CompRess: Self-Supervised Learning by Compressing Representations

Self-supervised learning aims to learn good representations with unlabel...

Soroush Abbasi Koohpayegani, et al. ∙

∙ 12/26/2019

A simple baseline for domain adaptation using rotation prediction

Recently, domain adaptation has become a hot research area with lots of ...

Ajinkya Tejankar, et al. ∙