219 citations · 225 across the 12 of their papers we have counts for
13 papers · 1 filter
Supervised Contrastive Prototype Learning: Augmentation Free Robust Neural Network
Iordanis Fostiropoulos, Laurent Itti
Transformations in the input space of Deep Neural Networks (DNN) lead to unintended changes in the feature space. Almost perceptually identical inputs, such as adversarial examples…
Model2Detector: Widening the Information Bottleneck for Out-of-Distribution Detection using a Handful of Gradient Steps
Sumedh A Sontakke, Buvaneswari Ramanan, Laurent Itti +1
Out-of-distribution detection is an important capability that has long eluded vanilla neural networks. Deep Neural networks (DNNs) tend to generate over-confident predictions when…
GalilAI: Out-of-Task Distribution Detection using Causal Active Experimentation for Safe Transfer RL
Sumedh A Sontakke, Stephen Iota, Zizhao Hu +3
Out-of-distribution (OOD) detection is a well-studied topic in supervised learning. Extending the successes in supervised learning methods to the reinforcement learning (RL) settin…
Lifelong Learning Without a Task Oracle
Amanda Rios, Laurent Itti
Supervised deep neural networks are known to undergo a sharp decline in the accuracy of older tasks when new tasks are learned, termed "catastrophic forgetting". Many state-of-the-…
Causal Curiosity: RL Agents Discovering Self-supervised Experiments for Causal Representation Learning
Sumedh A. Sontakke, Arash Mehrjou, Laurent Itti +1
Animals exhibit an innate ability to learn regularities of the world through interaction. By performing experiments in their environment, they are able to discern the causal factor…
Attentive Feature Reuse for Multi Task Meta learning
Kiran Lekkala, Laurent Itti
We develop new algorithms for simultaneous learning of multiple tasks (e.g., image classification, depth estimation), and for adapting to unseen task/domain distributions within th…