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Found 5 papers, 3 papers with code
Independent Component Alignment for Multi-task Learning
We present a novel gradient-based multi-task learning (MTL) approach that balances training in multi-task systems by aligning the independent components of the training objective.
Towards General Purpose Geometry-Preserving Single-View Depth Estimation
Our work shows that a model trained on this data along with conventional datasets can gain accuracy while predicting correct scene geometry.
Learning High-Resolution Domain-Specific Representations with a GAN Generator
Based on this finding, we propose LayerMatch scheme for approximating the representation of a GAN generator that can be used for unsupervised domain-specific pretraining.
Decoder Modulation for Indoor Depth Completion
Our second contribution is a novel training strategy that allows us to train on a semi-dense sensor data when the ground truth depth map is not available.
Ranked #1 on
Depth Completion
on Matterport3D
Double Refinement Network for Efficient Indoor Monocular Depth Estimation
Monocular depth estimation is the task of obtaining a measure of distance for each pixel using a single image.
Indoor Monocular Depth Estimation
Monocular Depth Estimation
