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Found 4 papers, 3 papers with code
TiCo: Transformation Invariance and Covariance Contrast for Self-Supervised Visual Representation Learning
Similar to other recent self-supervised learning methods, our method is based on maximizing the agreement among embeddings of different distorted versions of the same image, which pushes the encoder to produce transformation invariant representations.
Model-Predictive Policy Learning with Uncertainty Regularization for Driving in Dense Traffic
Learning a policy using only observational data is challenging because the distribution of states it induces at execution time may differ from the distribution observed during training.
CortexNet: a Generic Network Family for Robust Visual Temporal Representations
In the past five years we have observed the rise of incredibly well performing feed-forward neural networks trained supervisedly for vision related tasks.
An Analysis of Deep Neural Network Models for Practical Applications
Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art.
