2 citations · 4 across the 6 of their papers we have counts for
6 papers · 1 filter
Active Learning with a Noisy Annotator
Netta Shafir, Guy Hacohen, Daphna Weinshall
Active Learning (AL) aims to reduce annotation costs by strategically selecting the most informative samples for labeling. However, most active learning methods struggle in the low…
Same accuracy, twice as fast: continuous training surpasses retraining from scratch
Eli Verwimp, Guy Hacohen, Tinne Tuytelaars
Continual learning aims to enable models to adapt to new datasets without losing performance on previously learned data, often assuming that prior data is no longer available. Howe…
Predicting the Susceptibility of Examples to Catastrophic Forgetting
Guy Hacohen, Tinne Tuytelaars
Catastrophic forgetting - the tendency of neural networks to forget previously learned data when learning new information - remains a central challenge in continual learning. In th…
Pruning the Unlabeled Data to Improve Semi-Supervised Learning
Guy Hacohen, Daphna Weinshall
In the domain of semi-supervised learning (SSL), the conventional approach involves training a learner with a limited amount of labeled data alongside a substantial volume of unlab…
How to Select Which Active Learning Strategy is Best Suited for Your Specific Problem and Budget
Guy Hacohen, Daphna Weinshall
In the domain of Active Learning (AL), a learner actively selects which unlabeled examples to seek labels from an oracle, while operating within predefined budget constraints. Impo…
On The Power of Curriculum Learning in Training Deep Networks
Guy Hacohen, Daphna Weinshall
Training neural networks is traditionally done by providing a sequence of random mini-batches sampled uniformly from the entire training data. In this work, we analyze the effect o…