Search Results for author:
Found 8 papers, 3 papers with code
ReCoRe: Regularized Contrastive Representation Learning of World Model
To address these challenges, we present a world model that learns invariant features using (i) contrastive unsupervised learning and (ii) an intervention-invariant regularizer.
LanGWM: Language Grounded World Model
Furthermore, our proposed technique of explicit language-grounded visual representation learning has the potential to improve models for human-robot interaction because our extracted visual features are language grounded.
Model-based Reinforcement Learning
Out-of-Distribution Generalization
+2
Contrastive Unsupervised Learning of World Model with Invariant Causal Features
In particular, we use contrastive unsupervised learning to learn the invariant causal features, which enforces invariance across augmentations of irrelevant parts or styles of the observation.
A Stochastic Bundle Method for Interpolating Networks
We propose a novel method for training deep neural networks that are capable of interpolation, that is, driving the empirical loss to zero.
Embodied Visual Navigation with Automatic Curriculum Learning in Real Environments
We present NavACL, a method of automatic curriculum learning tailored to the navigation task.
Fast-SCNN: Fast Semantic Segmentation Network
The encoder-decoder framework is state-of-the-art for offline semantic image segmentation.
Ranked #7 on
Semantic Segmentation
on SynPASS
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time
Modern deep learning architectures produce highly accurate results on many challenging semantic segmentation datasets.
Ranked #79 on
Semantic Segmentation
on Cityscapes val
Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation
In cardiac magnetic resonance imaging, fully-automatic segmentation of the heart enables precise structural and functional measurements to be taken, e. g. from short-axis MR images of the left-ventricle.
