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Found 2 papers, 2 papers with code
Logistic-Normal Likelihoods for Heteroscedastic Label Noise
A natural way of estimating heteroscedastic label noise in regression is to model the observed (potentially noisy) target as a sample from a normal distribution, whose parameters can be learned by minimizing the negative log-likelihood.
LightDepth: A Resource Efficient Depth Estimation Approach for Dealing with Ground Truth Sparsity via Curriculum Learning
Our approach devises model-agnostic curriculum-based learning for depth estimation.
Ranked #1 on
Depth Estimation
on KITTI Eigen split
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