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Found 10 papers, 4 papers with code
Diagnosing Catastrophe: Large parts of accuracy loss in continual learning can be accounted for by readout misalignment
Unlike primates, training artificial neural networks on changing data distributions leads to a rapid decrease in performance on old tasks.
Keep Moving: identifying task-relevant subspaces to maximise plasticity for newly learned tasks
Continual learning algorithms strive to acquire new knowledge while preserving prior information.
Fast Concept Mapping: The Emergence of Human Abilities in Artificial Neural Networks when Learning Embodied and Self-Supervised
Following this, the representations learned through interaction with the world can be used to associate semantic concepts such as different types of doors.
WestDrive X LoopAR: An open-access virtual reality project in Unity for evaluating user interaction methods during TOR
The presented project contains all needed functionalities for realistic traffic behavior, cars, and pedestrians, as well as a large, open-source, scriptable, and modular VR environment.
Human-Computer Interaction
Learning Semantically Meaningful Representations Through Embodiment
How do humans acquire a meaningful understanding of the world with little to no supervision or semantic labels provided by the environment?
Enhancing Traffic Scene Predictions with Generative Adversarial Networks
This ensures the quality of the predicted frames to be sufficient to enable accurate detection of objects, which is especially important for autonomously driving cars.
Further advantages of data augmentation on convolutional neural networks
As a matter of fact, convolutional neural networks for image object classification are typically trained with both data augmentation and explicit regularization, assuming the benefits of all techniques are complementary.
Learning robust visual representations using data augmentation invariance
Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream.
Data augmentation instead of explicit regularization
Despite the fact that some (explicit) regularization techniques, such as weight decay and dropout, require costly fine-tuning of sensitive hyperparameters, the interplay between them and other elements that provide implicit regularization is not well understood yet.
Do deep nets really need weight decay and dropout?
The impressive success of modern deep neural networks on computer vision tasks has been achieved through models of very large capacity compared to the number of available training examples.
