Search Results for author: Shashanka Venkataramanan

Found 5 papers, 2 papers with code

AlignMix: Improving representations by interpolating aligned features

no code implementations29 Sep 2021 Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis

Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels.

Data Augmentation Representation Learning

It Takes Two to Tango: Mixup for Deep Metric Learning

1 code implementation ICLR 2022 Shashanka Venkataramanan, Bill Psomas, Ewa Kijak, Laurent Amsaleg, Konstantinos Karantzalos, Yannis Avrithis

In this work, we aim to bridge this gap and improve representations using mixup, which is a powerful data augmentation approach interpolating two or more examples and corresponding target labels at a time.

Ranked #4 on Metric Learning on In-Shop (using extra training data)

Data Augmentation Metric Learning +1

AlignMixup: Improving Representations By Interpolating Aligned Features

1 code implementation29 Mar 2021 Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis

Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels.

Data Augmentation Representation Learning +1

Enhancing Visual Representations for Efficient Object Recognition during Online Distillation

no code implementations1 Jan 2021 Shashanka Venkataramanan, Bruce W McIntosh, Abhijit Mahalanobis

Exploiting this fact, we aim to reduce the computations of our framework by employing a binary student network (BSN) to learn the frequently occurring classes using the pseudo-labels generated by the teacher network (TN) on an unlabeled image stream.

Object Recognition Outlier Detection

Attention Guided Anomaly Localization in Images

no code implementations ECCV 2020 Shashanka Venkataramanan, Kuan-Chuan Peng, Rajat Vikram Singh, Abhijit Mahalanobis

Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information.

Ranked #23 on Anomaly Detection on MVTec AD (Segmentation AUROC metric, using extra training data)

Anomaly Detection

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