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Found 9 papers, 3 papers with code
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization
In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level subproblem is for scalar optimization.
BYOM: Building Your Own Multi-Task Model For Free
Recently, various merging methods have been proposed to build a multi-task model from task-specific finetuned models without retraining.
Efficient Transfer Learning in Diffusion Models via Adversarial Noise
Diffusion Probabilistic Models (DPMs) have demonstrated substantial promise in image generation tasks but heavily rely on the availability of large amounts of training data.
Dual-Balancing for Multi-Task Learning
Multi-task learning (MTL), a learning paradigm to learn multiple related tasks simultaneously, has achieved great success in various fields.
LibMTL: A Python Library for Multi-Task Learning
This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi-Task Learning (MTL).
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning
Multi-Task Learning (MTL) has achieved success in various fields.
Multi-Objective Meta Learning
Empirically, we show the effectiveness of the proposed MOML framework in several meta learning problems, including few-shot learning, neural architecture search, domain adaptation, and multi-task learning.
Multi-Task Adversarial Attack
More specifically, MTA uses a generator for adversarial perturbations which consists of a shared encoder for all tasks and multiple task-specific decoders.
Effective, Efficient and Robust Neural Architecture Search
Although NAS methods can find network architectures with the state-of-the-art performance, the adversarial robustness and resource constraint are often ignored in NAS.
