no code implementations • 12 Oct 2023 • Shashanka Venkataramanan, Mamshad Nayeem Rizve, João Carreira, Yuki M. Asano, Yannis Avrithis
But are we making the best use of data?
no code implementations • 5 Jan 2023 • Shashanka Venkataramanan, Amir Ghodrati, Yuki M. Asano, Fatih Porikli, Amirhossein Habibian
This work aims to improve the efficiency of vision transformers (ViT).
no code implementations • 29 Jun 2022 • Shashanka Venkataramanan, Ewa Kijak, Laurent Amsaleg, Yannis Avrithis
Finally, to address inconsistencies due to linear target interpolation, we introduce a self-distillation approach to generate and interpolate synthetic targets.
no code implementations • 29 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.
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 #8 on Metric Learning on CUB-200-2011 (using extra training data)
2 code implementations • CVPR 2022 • 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.
Ranked #1 on Representation Learning on CIFAR10
no code implementations • 1 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.
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 #74 on Anomaly Detection on MVTec AD (Segmentation AUROC metric)