Auxiliary Learning

24 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 )

Latest papers with no code

Auxiliary Tasks Enhanced Dual-affinity Learning for Weakly Supervised Semantic Segmentation

no code yet • 2 Mar 2024

We propose AuxSegNet+, a weakly supervised auxiliary learning framework to explore the rich information from these saliency maps and the significant inter-task correlation between saliency detection and semantic segmentation.

Two-Stage Multi-task Self-Supervised Learning for Medical Image Segmentation

no code yet • 11 Feb 2024

Self-supervised learning offers a solution by creating auxiliary learning tasks from the available dataset and then leveraging the knowledge acquired from solving auxiliary tasks to help better solve the target segmentation task.

A Survey on Cross-Domain Sequential Recommendation

no code yet • 10 Jan 2024

Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain).

Mitigate Domain Shift by Primary-Auxiliary Objectives Association for Generalizing Person ReID

no code yet • 24 Oct 2023

While deep learning has significantly improved ReID model accuracy under the independent and identical distribution (IID) assumption, it has also become clear that such models degrade notably when applied to an unseen novel domain due to unpredictable/unknown domain shift.

Perception Reinforcement Using Auxiliary Learning Feature Fusion: A Modified Yolov8 for Head Detection

no code yet • 14 Oct 2023

Head detection provides distribution information of pedestrian, which is crucial for scene statistical analysis, traffic management, and risk assessment and early warning.

Disentangled Latent Spaces Facilitate Data-Driven Auxiliary Learning

no code yet • 13 Oct 2023

In this paper, we propose a novel framework, dubbed Detaux, whereby a weakly supervised disentanglement procedure is used to discover new unrelated classification tasks and the associated labels that can be exploited with the principal task in any Multi-Task Learning (MTL) model.

Semantic-aware Temporal Channel-wise Attention for Cardiac Function Assessment

no code yet • 9 Oct 2023

Cardiac function assessment aims at predicting left ventricular ejection fraction (LVEF) given an echocardiogram video, which requests models to focus on the changes in the left ventricle during the cardiac cycle.

Predictive auxiliary objectives in deep RL mimic learning in the brain

no code yet • 9 Oct 2023

Here, we study the effects predictive auxiliary objectives have on representation learning across different modules of an RL system and how these mimic representational changes observed in the brain.

Asset Bundling for Wind Power Forecasting

no code yet • 28 Sep 2023

This approach effectively introduces an auxiliary learning task (predicting the bundle-level time series) to help the main learning tasks.

Large Language Models for Compiler Optimization

no code yet • 11 Sep 2023

We explore the novel application of Large Language Models to code optimization.