Multi-Task Learning
1074 papers with code • 6 benchmarks • 54 datasets
Multi-task learning aims to learn multiple different tasks simultaneously while maximizing performance on one or all of the tasks.
( Image credit: Cross-stitch Networks for Multi-task Learning )
Libraries
Use these libraries to find Multi-Task Learning models and implementationsLatest papers with no code
FastCAR: Fast Classification And Regression Multi-Task Learning via Task Consolidation for Modelling a Continuous Property Variable of Object Classes
FastCAR involves a labeling transformation approach that can be used with a single-task regression network architecture.
Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation
In this paper, we study the MTL problem with hybrid targets for the first time and propose the model named Hybrid Targets Learning Network (HTLNet) to explore task dependence and enhance optimization.
Enhanced Facet Generation with LLM Editing
The second strategy is to enhance the facets by combining Large Language Model (LLM) and the small model.
Joint chest X-ray diagnosis and clinical visual attention prediction with multi-stage cooperative learning: enhancing interpretability
As deep learning has become the state-of-the-art for computer-assisted diagnosis, interpretability of the automatic decisions is crucial for clinical deployment.
Multi-Task Learning with Multi-Task Optimization
Multi-task learning solves multiple correlated tasks.
Leveraging Large Language Model-based Room-Object Relationships Knowledge for Enhancing Multimodal-Input Object Goal Navigation
In this study, we propose a data-driven, modular-based approach, trained on a dataset that incorporates common-sense knowledge of object-to-room relationships extracted from a large language model.
Volumetric Environment Representation for Vision-Language Navigation
To achieve a comprehensive 3D representation with fine-grained details, we introduce a Volumetric Environment Representation (VER), which voxelizes the physical world into structured 3D cells.
M3: A Multi-Task Mixed-Objective Learning Framework for Open-Domain Multi-Hop Dense Sentence Retrieval
In recent research, contrastive learning has proven to be a highly effective method for representation learning and is widely used for dense retrieval.
Open Knowledge Base Canonicalization with Multi-task Learning
MulCanon unifies the learning objectives of these sub-tasks, and adopts a two-stage multi-task learning paradigm for training.
Human Detection in Realistic Through-the-Wall Environments using Raw Radar ADC Data and Parametric Neural Networks
The radar signal processing algorithm is one of the core components in through-wall radar human detection technology.