Auxiliary Learning
30 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
Self-Supervised Generalisation with Meta Auxiliary Learning
The loss for the label-generation network incorporates the loss of the multi-task network, and so this interaction between the two networks can be seen as a form of meta learning with a double gradient.
Learning Object Placements For Relational Instructions by Hallucinating Scene Representations
One particular requirement for such robots is that they are able to understand spatial relations and can place objects in accordance with the spatial relations expressed by their user.
AANG: Automating Auxiliary Learning
ldery/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.
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}.
RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
Algolzw/SPAC-Deformable-Registration
•
•
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.
Deep Auxiliary Learning for Visual Localization and Odometry
decayale/vlocnet
•
•
We evaluate our proposed VLocNet on indoor as well as outdoor datasets and show that even our single task model exceeds the performance of state-of-the-art deep architectures for global localization, while achieving competitive performance for visual odometry estimation.
Dataset2Vec: Learning Dataset Meta-Features
As a data-driven approach, meta-learning requires meta-features that represent the primary learning tasks or datasets, and are estimated traditonally as engineered dataset statistics that require expert domain knowledge tailored for every meta-task.
Auxiliary Learning by Implicit Differentiation
AvivNavon/AuxiLearn
•
•
ICLR 2021
Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary tasks; and (ii) combining auxiliary tasks into a single coherent loss.
Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs
Our proposed method is learning to learn a primary task by predicting meta-paths as auxiliary tasks.
Self-supervised Auxiliary Learning for Graph Neural Networks via Meta-Learning
mlvlab/SELAR
•
•
Our method is learning to learn a primary task with various auxiliary tasks to improve generalization performance.
