150 citations · 488 across the 33 of their papers we have counts for
36 papers
Follow the Geometry, Not the Model: Cold Start Semi-Supervised Learning
Itai David, Daphna Weinshall
Modern semi-supervised learning (SSL) couples pseudo-label generation and classifier training, using the classifier's own confidence to select the pseudo-labels that are then used…
Coordination on a Budget: Federated Active Learning with Few Labels
Liam Mohr, Daphna Weinshall
Federated Active Learning (FAL) addresses the dual challenges of data privacy and label scarcity, where the absence of a global data view introduces additional hurdles for coordina…
VISTA: Validation-Informed Trajectory Adaptation via Self-Distillation
Eli Corn, Daphna Weinshall
Deep learning models may converge to suboptimal solutions despite strong validation accuracy, masking an optimization failure we term Trajectory Deviation. This is because as train…
Leveraging Complementary Embeddings for Replay Selection in Continual Learning with Small Buffers
Danit Yanowsky, Daphna Weinshall
Catastrophic forgetting remains a key challenge in Continual Learning (CL). In replay-based CL with severe memory constraints, performance critically depends on the sample selectio…
Forget Me Not: Fighting Local Overfitting with Knowledge Fusion and Distillation
Uri Stern, Eli Corn, Daphna Weinshall
Overfitting in deep neural networks occurs less frequently than expected. This is a puzzling observation, as theory predicts that greater model capacity should eventually lead to o…
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…
On Local Overfitting and Forgetting in Deep Neural Networks
Uri Stern, Tomer Yaacoby, Daphna Weinshall
The infrequent occurrence of overfitting in deep neural networks is perplexing: contrary to theoretical expectations, increasing model size often enhances performance in practice.…
DCoM: Active Learning for All Learners
Inbal Mishal, Daphna Weinshall
Deep Active Learning (AL) techniques can be effective in reducing annotation costs for training deep models. However, their effectiveness in low- and high-budget scenarios seems to…
TEAL: New Selection Strategy for Small Buffers in Experience Replay Class Incremental Learning
Shahar Shaul-Ariel, Daphna Weinshall
Continual Learning is an unresolved challenge, whose relevance increases when considering modern applications. Unlike the human brain, trained deep neural networks suffer from a ph…
Relearning Forgotten Knowledge: on Forgetting, Overfit and Training-Free Ensembles of DNNs
Uri Stern, Daphna Weinshall
The infrequent occurrence of overfit in deep neural networks is perplexing. On the one hand, theory predicts that as models get larger they should eventually become too specialized…
United We Stand: Using Epoch-wise Agreement of Ensembles to Combat Overfit
Uri Stern, Daniel Shwartz, Daphna Weinshall
Deep neural networks have become the method of choice for solving many classification tasks, largely because they can fit very complex functions defined over raw data. The downside…
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…
The Dynamic of Consensus in Deep Networks and the Identification of Noisy Labels
Daniel Shwartz, Uri Stern, Daphna Weinshall
Deep neural networks have incredible capacity and expressibility, and can seemingly memorize any training set. This introduces a problem when training in the presence of noisy labe…
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…
More Is More -- Narrowing the Generalization Gap by Adding Classification Heads
Roee Cates, Daphna Weinshall
Overfit is a fundamental problem in machine learning in general, and in deep learning in particular. In order to reduce overfit and improve generalization in the classification of…
Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
Boaz Lerner, Guy Shiran, Daphna Weinshall
Recently, Semi-Supervised Learning (SSL) has shown much promise in leveraging unlabeled data while being provided with very few labels. In this paper, we show that ignoring the lab…
Multiclass non-Adversarial Image Synthesis, with Application to Classification from Very Small Sample
Itamar Winter, Daphna Weinshall
The generation of synthetic images is currently being dominated by Generative Adversarial Networks (GANs). Despite their outstanding success in generating realistic looking images,…
Generative Latent Implicit Conditional Optimization when Learning from Small Sample
Idan Azuri, Daphna Weinshall
We revisit the long-standing problem of learning from a small sample, to which end we propose a novel method called GLICO (Generative Latent Implicit Conditional Optimization). GLI…
Multi-Modal Deep Clustering: Unsupervised Partitioning of Images
Guy Shiran, Daphna Weinshall
The clustering of unlabeled raw images is a daunting task, which has recently been approached with some success by deep learning methods. Here we propose an unsupervised clustering…