Auxiliary Learning
25 papers with code • 0 benchmarks • 0 datasets
Auxiliary learning aims to find or design auxiliary tasks which can improve the performance on one or some primary tasks.
( Image credit: Self-Supervised Generalisation with Meta Auxiliary Learning )
Benchmarks
These leaderboards are used to track progress in Auxiliary Learning
Most implemented papers
Learning to Recover Spectral Reflectance from RGB Images
Instead of relying on naive end-to-end training, we also propose a novel architecture that integrates the physical relationship between the spectral reflectance and the corresponding RGB images into the network based on our mathematical analysis.
Image-to-Image Translation with Deep Reinforcement Learning
Algolzw/SPAC-Deformable-Registration
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The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image.
Enhancing Molecular Property Prediction with Auxiliary Learning and Task-Specific Adaptation
Pretrained Graph Neural Networks have been widely adopted for various molecular property prediction tasks.
GeoAuxNet: Towards Universal 3D Representation Learning for Multi-sensor Point Clouds
zhangshengjun2019/geoauxnet
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In this paper, we propose geometry-to-voxel auxiliary learning to enable voxel representations to access point-level geometric information, which supports better generalisation of the voxel-based backbone with additional interpretations of multi-sensor point clouds.
