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Branched Multi-Task Networks: Deciding what layers to share

https://doi.org/10.5244/c.34.59

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A Modulation Module for Multi-task Learning with Applications in Image Retrieval

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Dynamic Task Prioritization for Multitask Learning

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Multitask Learning

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Cross-Stitch Networks for Multi-task Learning

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Fully-Adaptive Feature Sharing in Multi-Task Networks with Applications in Person Attribute Classification

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UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory

inferredcites →https://doi.org/10.1109/cvpr.2018.00077openalex-snapshot

PAD-Net: Multi-tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing

inferredcites →https://doi.org/10.1109/cvpr.2018.00391openalex-snapshot

Taskonomy: Disentangling Task Transfer Learning

inferredcites →https://doi.org/10.1109/cvpr.2018.00432openalex-snapshot

Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning

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End-To-End Multi-Task Learning With Attention

inferredcites →https://doi.org/10.1109/cvpr.2019.00332openalex-snapshot

NDDR-CNN: Layerwise Feature Fusing in Multi-Task CNNs by Neural Discriminative Dimensionality Reduction

inferredcites →https://doi.org/10.1109/cvpr.2019.01267openalex-snapshot

Representation Similarity Analysis for Efficient Task Taxonomy & Transfer Learning

inferredcites →https://doi.org/10.1109/iccv.2015.425openalex-snapshot

Deep Learning Face Attributes in the Wild

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Stochastic Filter Groups for Multi-Task CNNs: Learning Specialist and Generalist Convolution Kernels

inferredcites →https://doi.org/10.1109/iccv.2019.00653openalex-snapshot

Task2Vec: Task Embedding for Meta-Learning

inferredcites →https://doi.org/10.1109/tpami.2015.2389824openalex-snapshot

Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

inferredcites →https://doi.org/10.1111/j.1467-9868.2005.00532.xopenalex-snapshot

Model Selection and Estimation in Regression with Grouped Variables

inferredcites →https://doi.org/10.1113/jphysiol.2007.143040openalex-snapshot

Direction selectivity in V1 of alert monkeys: evidence for parallel pathways for motion processing

inferredcites →https://doi.org/10.1145/1014052.1014067openalex-snapshot

Regularized multi--task learning

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Distributed Multi-Task Relationship Learning

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Evolutionary architecture search for deep multitask networks

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Attributes for Improved Attributes: A Multi-Task Network Utilizing Implicit and Explicit Relationships for Facial Attribute Classification

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Regularized Evolution for Image Classifier Architecture Search

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A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization

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Cityscapes dataset for semantic urban scene understanding

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Neural Architecture Search with Reinforcement Learning

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An Overview of Multi-Task Learning in Deep Neural Networks

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Fast Scene Understanding for Autonomous Driving

inferredcites →https://doi.org/10.48550/arxiv.1802.03268openalex-snapshot

Efficient Neural Architecture Search via Parameter Sharing

inferredcites →https://doi.org/10.48550/arxiv.1804.06964openalex-snapshot

GNAS: A Greedy Neural Architecture Search Method for Multi-Attribute Learning

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Neural Architecture Search: A Survey

inferredcites →https://doi.org/10.48550/arxiv.1810.04650openalex-snapshot

Multi-Task Learning as Multi-Objective Optimization

inferredcites →https://doi.org/10.48550/arxiv.1908.04339openalex-snapshot

Feature Partitioning for Efficient Multi-Task Architectures

inferredcites →https://doi.org/10.7551/mitpress/7503.003.0010openalex-snapshot

Multi-Task Feature Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Meta Learning and the AI Learning Process

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Training of Multiple and Mixed Tasks with a Single Network Using Feature Modulation

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Self-supervised Multi-task Representation Learning for Sequential Medical Images

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Federated Contrastive Learning for Decentralized Unlabeled Medical Images

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Capacity Re-Balancing in Multi-Task Dense Prediction via Low-Rank Experts and Task-Specific Attentive Projection

inferred← citeshttps://doi.org/10.1109/cvpr46437.2021.00680openalex-snapshot

CompositeTasking: Understanding Images by Spatial Composition of Tasks

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Detailed Delineation of the Fetal Brain in Diffusion MRI via Multi-Task Learning

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Learning Task-Preferred Inference Routes for Gradient De-Conflict in Multi-Output DNNs

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Toward Unified Expertise: Learning a Single Vision Model From Diverse Perception

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ADA-AT/DT: An Adversarial Approach for Cross-Domain and Cross-Task Knowledge Transfer

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Multi-Task Classification of Sewer Pipe Defects and Properties using a Cross-Task Graph Neural Network Decoder

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Scalable Multitask Learning Using Gradient-based Estimation of Task Affinity

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Adversarial Robustness in Multi-Task Learning: Promises and Illusions

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Learning Conflict-Noticed Architecture for Multi-Task Learning

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Toward a generic federated learning platform optimized for computer vision applications

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A Multi-task Learning Model for Daily Activity Forecast in Smart Home

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Transformation based Parallel Forecasting of Individual Gas Consumption from Smart Meter Data

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AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning

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Regularizing Deep Multi-Task Networks using Orthogonal Gradients

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Gradient Surgery for Multi-Task Learning

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Deeper Task-Specificity Improves Joint Entity and Relation Extraction

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Learning to Branch for Multi-Task Learning

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What makes instance discrimination good for transfer learning?

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Small Towers Make Big Differences

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Multi-Task Learning with Deep Neural Networks: A Survey

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Deep Transformers with Latent Depth

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Measuring and Harnessing Transference in Multi-Task Learning

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Collaborative Intelligence: Challenges and Opportunities

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Instance-Level Task Parameters: A Robust Multi-task Weighting Framework

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Efficiently Identifying Task Groupings for Multi-Task Learning

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Cross-Task Consistency Learning Framework for Multi-Task Learning

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Apprentissage automatique pour déduire le comportement des utilisateurs dans les réseaux autonomes 5G

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