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Found 6 papers, 2 papers with code
Autoencoder for Synthetic to Real Generalization: From Simple to More Complex Scenes
Learning on synthetic data and transferring the resulting properties to their real counterparts is an important challenge for reducing costs and increasing safety in machine learning.
Autoencoder Attractors for Uncertainty Estimation
While input images close to known samples will converge to the same or similar attractor, input samples containing unknown features are unstable and converge to different training samples by potentially removing or changing characteristic features.
Autoencoder Based Inter-Vehicle Generalization for In-Cabin Occupant Classification
Common domain shift problem formulations consider the integration of multiple source domains, or the target domain during training.
Illumination Normalization by Partially Impossible Encoder-Decoder Cost Function
Our method exploits the availability of identical sceneries under different illumination and environmental conditions for which we formulate a partially impossible reconstruction target: the input image will not convey enough information to reconstruct the target in its entirety.
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation
In this article we present a dense correspondence field approach that is much less outlier-prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields.
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation
In this paper we present a dense correspondence field approach that is much less outlier prone and thus much better suited for optical flow estimation than approximate nearest neighbor fields.
