Leveraging Cross-Task Relationships for Efficient Multi-Task Learning with Partial Supervision

Główne pojęcia

Joint-Task Regularization (JTR) leverages cross-task relationships to simultaneously regularize all tasks in a single joint-task latent space, improving learning when data is not fully labeled for all tasks.

Streszczenie

The content discusses the problem of multi-task learning (MTL) with partially labeled data. Most existing MTL methods require fully labeled datasets, which can be prohibitively expensive and impractical to obtain, especially for dense prediction tasks.
The authors propose a new approach called Joint-Task Regularization (JTR) to address this issue. JTR encodes predictions and labels for multiple tasks into a single joint-task latent space and regularizes the encoded features by a distance loss in this space. This allows information to flow across multiple tasks during regularization, leveraging cross-task relationships. Additionally, JTR scales linearly with the number of tasks, unlike previous pair-wise task regularization methods which scale quadratically.
The authors extensively benchmark JTR on variations of three popular MTL datasets - NYU-v2, Cityscapes, and Taskonomy - under different partially labeled scenarios. JTR outperforms existing methods across these benchmarks, demonstrating its effectiveness in achieving data-efficient multi-task learning.

Statystyki

Annotating a segmentation mask for a single image in the Cityscapes dataset took over 1.5 hours on average.
The NYU-v2 dataset has an additional 47,584 samples labeled only for the depth estimation task, which is approximately 60 times larger than the multi-task training split.

Cytaty

None

Kluczowe wnioski z

Joint-Task Regularization for Partially Labeled Multi-Task Learning

by Kento Nishi,... o arxiv.org 04-03-2024

https://arxiv.org/pdf/2404.01976.pdf

Głębsze pytania

How can JTR be extended to handle heterogeneous tasks beyond dense prediction, such as a combination of classification, regression, and pixel-level tasks?

In order to extend JTR to handle heterogeneous tasks beyond dense prediction, such as a combination of classification, regression, and pixel-level tasks, several modifications and adaptations can be made:

Task-specific Encoders: Instead of using a single encoder for all tasks, task-specific encoders can be introduced to extract features tailored to each task type. This way, the model can learn task-specific representations that are more suitable for classification, regression, or pixel-level tasks.

Task-specific Loss Functions: Different loss functions can be employed for each task type to optimize the model's performance on classification, regression, and pixel-level tasks. For example, cross-entropy loss can be used for classification tasks, mean squared error for regression tasks, and pixel-wise loss for pixel-level tasks.

Task Weighting: Assigning different weights to each task based on its importance or complexity can help the model prioritize certain tasks over others. This can be particularly useful when dealing with a mix of tasks with varying levels of difficulty.

Multi-Modal Fusion: If the heterogeneous tasks involve different modalities of data, such as images, text, and audio, JTR can be extended to incorporate multi-modal fusion techniques to effectively combine information from different sources.

By incorporating these adaptations, JTR can be tailored to handle a wide range of heterogeneous tasks beyond dense prediction, enabling it to effectively address the complexities and nuances of diverse task types in multi-task learning scenarios.

How can JTR be adapted to handle domain gaps when combining datasets from different sources for multi-task learning with partial supervision?

When combining datasets from different sources for multi-task learning with partial supervision, JTR can be adapted to handle domain gaps by implementing the following strategies:

Domain Adaptation Techniques: Utilize domain adaptation methods to align the distributions of data from different sources. Techniques such as adversarial training, domain adversarial neural networks, or domain-specific normalization layers can help mitigate domain gaps and improve model generalization across diverse datasets.

Transfer Learning: Pre-train the model on a source domain with abundant labeled data before fine-tuning on the target domain with partial supervision. Transfer learning can help the model leverage knowledge from the labeled source domain to improve performance on the target domain with limited supervision.

Data Augmentation: Augment the data from the target domain to increase its diversity and bridge the domain gaps. Techniques like random cropping, rotation, flipping, and color jittering can help the model learn robust representations that generalize well across different domains.

Task-Specific Adaptation: Implement task-specific adaptation mechanisms to account for domain gaps that affect certain tasks more than others. By adjusting the regularization or loss functions for specific tasks based on domain discrepancies, the model can learn more robust task-specific representations.

By incorporating these adaptation strategies, JTR can effectively handle domain gaps when combining datasets from different sources for multi-task learning with partial supervision, improving the model's performance and generalization across diverse domains.

Can prior knowledge about task relationships be incorporated into the JTR framework to further improve its performance?

Incorporating prior knowledge about task relationships into the JTR framework can significantly enhance its performance by leveraging domain-specific insights and guiding the model towards more informed learning decisions. Here are some ways to integrate prior knowledge into the JTR framework:

Task Dependency Graph: Construct a task dependency graph that captures the relationships between different tasks based on domain expertise or empirical observations. By encoding this graph into the model architecture, JTR can prioritize learning tasks that are more closely related and share dependencies.

Task-Specific Constraints: Introduce task-specific constraints or priors that reflect known relationships between tasks. For example, if certain tasks are known to be complementary or mutually exclusive, these constraints can be incorporated into the loss functions to guide the model towards more coherent predictions.

Knowledge Distillation: Use knowledge distillation techniques to transfer knowledge from a teacher model that encapsulates task relationships to the JTR model. By distilling task-specific insights and relationships from the teacher model, JTR can benefit from the distilled knowledge during training.

Semi-Supervised Learning with Prior Knowledge: Combine semi-supervised learning approaches with prior knowledge about task relationships to regularize the model's learning process. By incorporating task-specific consistency constraints based on prior knowledge, JTR can effectively leverage unlabeled data to improve performance on partially supervised tasks.

By integrating prior knowledge about task relationships into the JTR framework, the model can make more informed decisions, exploit task dependencies, and achieve better generalization and performance in multi-task learning scenarios.