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Found 8 papers, 5 papers with code
Multi-headed Neural Ensemble Search
Ensembles of CNN models trained with different seeds (also known as Deep Ensembles) are known to achieve superior performance over a single copy of the CNN.
Towards Understanding Adversarial Robustness of Optical Flow Networks
We show how these mistakes can be rectified in order to make optical flow networks robust to physical patch-based attacks.
Improving robustness against common corruptions with frequency biased models
CNNs perform remarkably well when the training and test distributions are i. i. d, but unseen image corruptions can cause a surprisingly large drop in performance.
Optimized Generic Feature Learning for Few-shot Classification across Domains
To learn models or features that generalize across tasks and domains is one of the grand goals of machine learning.
Understanding and Robustifying Differentiable Architecture Search
Differentiable Architecture Search (DARTS) has attracted a lot of attention due to its simplicity and small search costs achieved by a continuous relaxation and an approximation of the resulting bi-level optimization problem.
AutoDispNet: Improving Disparity Estimation With AutoML
In this work, we show how to use and extend existing AutoML techniques to efficiently optimize large-scale U-Net-like encoder-decoder architectures.
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation
Making use of the estimated occlusions, we also show improved results on motion segmentation and scene flow estimation.
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks
Particularly on small displacements and real-world data, FlowNet cannot compete with variational methods.
Dense Pixel Correspondence Estimation
Optical Flow Estimation
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