Auxiliary Learning

13 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 )

Most implemented papers

Self-Supervised Generalisation with Meta Auxiliary Learning

lorenmt/maxl NeurIPS 2019

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

mees/AIS-Alexa-Robot 23 Jan 2020

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.

Deep Auxiliary Learning for Visual Localization and Odometry

decayale/vlocnet 9 Mar 2018

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

hadijomaa/dataset2vec 27 May 2019

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

mlvlab/SELAR NeurIPS 2020

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 1 Mar 2021

Our method is learning to learn a primary task with various auxiliary tasks to improve generalization performance.

Auxiliary Tasks and Exploration Enable ObjectNav

joel99/objectnav 8 Apr 2021

We instead re-enable a generic learned agent by adding auxiliary learning tasks and an exploration reward.

Leveraging Auxiliary Tasks with Affinity Learning for Weakly Supervised Semantic Segmentation

xulianuwa/AuxSegNet ICCV 2021

Motivated by the significant inter-task correlation, we propose a novel weakly supervised multi-task framework termed as AuxSegNet, to leverage saliency detection and multi-label image classification as auxiliary tasks to improve the primary task of semantic segmentation using only image-level ground-truth labels.

Boost-RS: Boosted Embeddings for Recommender Systems and its Application to Enzyme-Substrate Interaction Prediction

hassounlab/boost-rs 28 Sep 2021

We show that each of our auxiliary tasks boosts learning of the embedding vectors, and that contrastive learning using Boost-RS outperforms attribute concatenation and multi-label learning.