• Corpus ID: 208513386

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

@article{Sun2019AdaShareLW,
  title={AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning},
  author={Ximeng Sun and Rameswar Panda and Rog{\'e}rio Schmidt Feris},
  journal={ArXiv},
  year={2019},
  volume={abs/1911.12423}
}
Multi-task learning is an open and challenging problem in computer vision. The typical way of conducting multi-task learning with deep neural networks is either through handcrafting schemes that share all initial layers and branch out at an adhoc point or through using separate task-specific networks with an additional feature sharing/fusion mechanism. Unlike existing methods, we propose an adaptive sharing approach, called AdaShare, that decides what to share across which tasks for achieving… 

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References

SHOWING 1-10 OF 74 REFERENCES

End-To-End Multi-Task Learning With Attention

The proposed Multi-Task Attention Network (MTAN) consists of a single shared network containing a global feature pool, together with a soft-attention module for each task, which allows learning of task-specific feature-level attention.

Many Task Learning With Task Routing

This paper introduces Many Task Learning (MaTL) as a special case of MTL where more than 20 tasks are performed by a single model and applies a conditional feature-wise transformation over the convolutional activations that enables a model to successfully perform a large number of tasks.

Branched Multi-Task Networks: Deciding what layers to share

This paper proposes an approach to automatically construct branched multi-task networks, by leveraging the employed tasks' affinities, given a specific budget, and generates architectures, in which shallow layers are task-agnostic, whereas deeper ones gradually grow more task-specific.

Deep Elastic Networks With Model Selection for Multi-Task Learning

This work proposes an efficient approach to exploit a compact but accurate model in a backbone architecture for each instance of all tasks to perform instance-wise dynamic network model selection for multi-task learning.

SpotTune: Transfer Learning Through Adaptive Fine-Tuning

In SpotTune, given an image from the target task, a policy network is used to make routing decisions on whether to pass the image through the fine-tuned layers or the pre-trained layers, which outperforms the traditional fine- Tuning approach on 12 out of 14 standard datasets.

Cross-Stitch Networks for Multi-task Learning

This paper proposes a principled approach to learn shared representations in Convolutional Networks using multitask learning using a new sharing unit: "cross-stitch" unit that combines the activations from multiple networks and can be trained end-to-end.

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

This paper proposes "stochastic filter groups" (SFG), a mechanism to assign convolution kernels in each layer to "specialist" and "generalist" groups, which are specific to and shared across different tasks, respectively.

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

Evaluation on person attributes classification tasks involving facial and clothing attributes suggests that the models produced by the proposed method are fast, compact and can closely match or exceed the state-of-the-art accuracy from strong baselines by much more expensive models.

Multi-task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

A principled approach to multi-task deep learning is proposed which weighs multiple loss functions by considering the homoscedastic uncertainty of each task, allowing us to simultaneously learn various quantities with different units or scales in both classification and regression settings.

Integrated perception with recurrent multi-task neural networks

This work proposes a new architecture, which it calls "MultiNet", in which not only deep image features are shared between tasks, but where tasks can interact in a recurrent manner by encoding the results of their analysis in a common shared representation of the data.
...