128 citations · 185 across the 12 of their papers we have counts for
12 papers
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…
Semi-Supervised Learning in the Few-Shot Zero-Shot Scenario
Noam Fluss, Guy Hacohen, Daphna Weinshall
Semi-Supervised Learning (SSL) is a framework that utilizes both labeled and unlabeled data to enhance model performance. Conventional SSL methods operate under the assumption that…
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…
Active Learning Through a Covering Lens
Ofer Yehuda, Avihu Dekel, Guy Hacohen +1
Deep active learning aims to reduce the annotation cost for the training of deep models, which is notoriously data-hungry. Until recently, deep active learning methods were ineffec…
Active Learning on a Budget: Opposite Strategies Suit High and Low Budgets
Guy Hacohen, Avihu Dekel, Daphna Weinshall
Investigating active learning, we focus on the relation between the number of labeled examples (budget size), and suitable querying strategies. Our theoretical analysis shows a beh…
The Grammar-Learning Trajectories of Neural Language Models
Leshem Choshen, Guy Hacohen, Daphna Weinshall +1
The learning trajectories of linguistic phenomena in humans provide insight into linguistic representation, beyond what can be gleaned from inspecting the behavior of an adult spea…
Principal Components Bias in Over-parameterized Linear Models, and its Manifestation in Deep Neural Networks
Guy Hacohen, Daphna Weinshall
Recent work suggests that convolutional neural networks of different architectures learn to classify images in the same order. To understand this phenomenon, we revisit the over-pa…
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…
Let's Agree to Agree: Neural Networks Share Classification Order on Real Datasets
Guy Hacohen, Leshem Choshen, Daphna Weinshall
We report a series of robust empirical observations, demonstrating that deep Neural Networks learn the examples in both the training and test sets in a similar order. This phenomen…