75 citations · 93 across the 6 of their papers we have counts for
17 papers
Is Multi-Task Learning an Upper Bound for Continual Learning?
Zihao Wu, Huy Tran, Hamed Pirsiavash +1
Continual and multi-task learning are common machine learning approaches to learning from multiple tasks. The existing works in the literature often assume multi-task learning as a…
A Simple Approach to Adversarial Robustness in Few-shot Image Classification
Akshayvarun Subramanya, Hamed Pirsiavash
Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable t…
SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation
K L Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar +1
Feature regression is a simple way to distill large neural network models to smaller ones. We show that with simple changes to the network architecture, regression can outperform m…
A Fistful of Words: Learning Transferable Visual Models from Bag-of-Words Supervision
Ajinkya Tejankar, Maziar Sanjabi, Bichen Wu +4
Using natural language as a supervision for training visual recognition models holds great promise. Recent works have shown that if such supervision is used in the form of alignmen…
Constrained Mean Shift for Representation Learning
Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Hamed Pirsiavash
We are interested in representation learning from labeled or unlabeled data. Inspired by recent success of self-supervised learning (SSL), we develop a non-contrastive representati…
Mean Shift for Self-Supervised Learning
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
Most recent self-supervised learning (SSL) algorithms learn features by contrasting between instances of images or by clustering the images and then contrasting between the image c…