7 citations · 14 across the 4 of their papers we have counts for
7 papers
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…
ISD: Self-Supervised Learning by Iterative Similarity Distillation
Ajinkya Tejankar, Soroush Abbasi Koohpayegani, Vipin Pillai +2
Recently, contrastive learning has achieved great results in self-supervised learning, where the main idea is to push two augmentations of an image (positive pairs) closer compared…
CompRess: Self-Supervised Learning by Compressing Representations
Soroush Abbasi Koohpayegani, Ajinkya Tejankar, Hamed Pirsiavash
Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller…