28 citations · 50 across the 6 of their papers we have counts for
5 papers · 1 filter
Disentangling the Effects of Data Augmentation and Format Transform in Self-Supervised Learning of Image Representations
Neha Kalibhat, Warren Morningstar, Alex Bijamov +3
Self-Supervised Learning (SSL) enables training performant models using limited labeled data. One of the pillars underlying vision SSL is the use of data augmentations/perturbation…
SASSL: Enhancing Self-Supervised Learning via Neural Style Transfer
Renan A. Rojas-Gomez, Karan Singhal, Ali Etemad +3
Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with…
Random Field Augmentations for Self-Supervised Representation Learning
Philip Andrew Mansfield, Arash Afkanpour, Warren Richard Morningstar +1
Self-supervised representation learning is heavily dependent on data augmentations to specify the invariances encoded in representations. Previous work has shown that applying dive…
Camera View Adjustment Prediction for Improving Image Composition
Yu-Chuan Su, Raviteja Vemulapalli, Ben Weiss +4
Image composition plays an important role in the quality of a photo. However, not every camera user possesses the knowledge and expertise required for capturing well-composed photo…
Contrastive Learning for Label-Efficient Semantic Segmentation
Xiangyun Zhao, Raviteja Vemulapalli, Philip Mansfield +4
Collecting labeled data for the task of semantic segmentation is expensive and time-consuming, as it requires dense pixel-level annotations. While recent Convolutional Neural Netwo…