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
Latest papers
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.
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.
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.
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.
MELTR: Meta Loss Transformer for Learning to Fine-tune Video Foundation Models
Therefore, we propose MEta Loss TRansformer (MELTR), a plug-in module that automatically and non-linearly combines various loss functions to aid learning the target task via auxiliary learning.
Enhancing Deep Knowledge Tracing with Auxiliary Tasks
In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i. e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}.
Auxiliary Learning as an Asymmetric Bargaining Game
Auxiliary learning is an effective method for enhancing the generalization capabilities of trained models, particularly when dealing with small datasets.
Benchmark for Uncertainty & Robustness in Self-Supervised Learning
hamanhbui/reliable_ssl_baselines
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Self-Supervised Learning (SSL) is crucial for real-world applications, especially in data-hungry domains such as healthcare and self-driving cars.
AANG: Automating Auxiliary Learning
Auxiliary objectives, supplementary learning signals that are introduced to help aid learning on data-starved or highly complex end-tasks, are commonplace in machine learning.
Counting with Adaptive Auxiliary Learning
smallmax00/counting_with_adaptive_auxiliary
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This paper proposes an adaptive auxiliary task learning based approach for object counting problems.
