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
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Latest papers with no code
Unified Convergence Theory of Stochastic and Variance-Reduced Cubic Newton Methods
Our helper framework offers the algorithm designer high flexibility for constructing and analyzing the stochastic Cubic Newton methods, allowing arbitrary size batches, and the use of noisy and possibly biased estimates of the gradients and Hessians, incorporating both the variance reduction and the lazy Hessian updates.
IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection
Firstly, to enhance the model's processing ability for different scale targets, a multi-scale perception module based on dilated convolution is designed, and the depth features containing multi-scale information are re-refined from both spatial and channel directions considering the inconsistency between feature maps of different scales.
Auxiliary Learning as a step towards Artificial General Intelligence
Auxiliary Learning is a machine learning approach in which the model acknowledges the existence of objects that do not come under any of its learned categories. The name Auxiliary learning was chosen due to the introduction of an auxiliary class.
Entire Space Counterfactual Learning: Tuning, Analytical Properties and Industrial Applications
As a basic research problem for building effective recommender systems, post-click conversion rate (CVR) estimation has long been plagued by sample selection bias and data sparsity issues.
Federated Learning with Server Learning: Enhancing Performance for Non-IID Data
Federated Learning (FL) has emerged as a means of distributed learning using local data stored at clients with a coordinating server.
Test-time Adaptation for Real Image Denoising via Meta-transfer Learning
We explore a different direction where we propose to improve real image denoising performance through a better learning strategy that can enable test-time adaptation on the multi-task network.
Meta Auxiliary Learning for Low-resource Spoken Language Understanding
Spoken language understanding (SLU) treats automatic speech recognition (ASR) and natural language understanding (NLU) as a unified task and usually suffers from data scarcity.
Enhancing Sequential Recommendation with Graph Contrastive Learning
Specifically, GCL4SR employs a Weighted Item Transition Graph (WITG), built based on interaction sequences of all users, to provide global context information for each interaction and weaken the noise information in the sequence data.
Boost Test-Time Performance with Closed-Loop Inference
Motivated by this, we propose to predict those hard-classified test samples in a looped manner to boost the model performance.
Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation
To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths.
