431 citations · 844 across the 46 of their papers we have counts for
8 papers · 1 filter
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…
SHERLock: Self-Supervised Hierarchical Event Representation Learning
Sumegh Roychowdhury, Sumedh A. Sontakke, Nikaash Puri +5
Temporal event representations are an essential aspect of learning among humans. They allow for succinct encoding of the experiences we have through a variety of sensory inputs. Al…
Beneficial Perturbation Network for designing general adaptive artificial intelligence systems
Shixian Wen, Amanda Rios, Yunhao Ge +1
The human brain is the gold standard of adaptive learning. It not only can learn and benefit from experience, but also can adapt to new situations. In contrast, deep neural network…
Beneficial Perturbations Network for Defending Adversarial Examples
Shixian Wen, Amanda Rios, Laurent Itti
Deep neural networks can be fooled by adversarial attacks: adding carefully computed small adversarial perturbations to clean inputs can cause misclassification on state-of-the-art…
Zero-shot Synthesis with Group-Supervised Learning
Yunhao Ge, Sami Abu-El-Haija, Gan Xin +1
Visual cognition of primates is superior to that of artificial neural networks in its ability to 'envision' a visual object, even a newly-introduced one, in different attributes in…
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…
Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition
Yunhao Ge, Jiaping Zhao, Laurent Itti
Object pose increases intraclass object variance which makes object recognition from 2D images harder. To render a classifier robust to pose variations, most deep neural networks t…