CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions

• URL: http://arxiv.org/abs/2401.02561v2
• Date: Thu, 07 Nov 2024 01:28:54 GMT
• Title: CONTRAST: Continual Multi-source Adaptation to Dynamic Distributions
• Authors: Sk Miraj Ahmed, Fahim Faisal Niloy, Xiangyu Chang, Dripta S. Raychaudhuri, Samet Oymak, Amit K. Roy-Chowdhury,
• Abstract summary: Continual Multi-source Adaptation to Dynamic Distributions (CONTRAST) is a novel method that optimally combines multiple source models to adapt to the dynamic test data. We show that the proposed method is able to optimally combine the source models and prioritize updates to the model least prone to forgetting.
• Score: 42.293444710522294
• License:
• Abstract: Adapting to dynamic data distributions is a practical yet challenging task. One effective strategy is to use a model ensemble, which leverages the diverse expertise of different models to transfer knowledge to evolving data distributions. However, this approach faces difficulties when the dynamic test distribution is available only in small batches and without access to the original source data. To address the challenge of adapting to dynamic distributions in such practical settings, we propose Continual Multi-source Adaptation to Dynamic Distributions (CONTRAST), a novel method that optimally combines multiple source models to adapt to the dynamic test data. CONTRAST has two distinguishing features. First, it efficiently computes the optimal combination weights to combine the source models to adapt to the test data distribution continuously as a function of time. Second, it identifies which of the source model parameters to update so that only the model which is most correlated to the target data is adapted, leaving the less correlated ones untouched; this mitigates the issue of ``forgetting" the source model parameters by focusing only on the source model that exhibits the strongest correlation with the test batch distribution. Through theoretical analysis we show that the proposed method is able to optimally combine the source models and prioritize updates to the model least prone to forgetting. Experimental analysis on diverse datasets demonstrates that the combination of multiple source models does at least as well as the best source (with hindsight knowledge), and performance does not degrade as the test data distribution changes over time (robust to forgetting).

Related papers

• AdaMerging: Adaptive Model Merging for Multi-Task Learning [68.75885518081357]
  This paper introduces an innovative technique called Adaptive Model Merging (AdaMerging) It aims to autonomously learn the coefficients for model merging, either in a task-wise or layer-wise manner, without relying on the original training data. Compared to the current state-of-the-art task arithmetic merging scheme, AdaMerging showcases a remarkable 11% improvement in performance.
  arXiv  Detail & Related papers  (2023-10-04T04:26:33Z)
• Building a Winning Team: Selecting Source Model Ensembles using a Submodular Transferability Estimation Approach [20.86345962679122]
  Estimating the transferability of publicly available pretrained models to a target task has assumed an important place for transfer learning tasks. We propose a novel Optimal tranSport-based suBmOdular tRaNsferability metric (OSBORN) to estimate the transferability of an ensemble of models to a downstream task.
  arXiv  Detail & Related papers  (2023-09-05T17:57:31Z)
• Parameter-efficient Modularised Bias Mitigation via AdapterFusion [22.424110883305243]
  We propose a novel approach to develop stand-alone debiasing functionalities separate from the model. We introduce DAM - a debiasing approach to first encapsulate arbitrary bias mitigation functionalities into separate adapters, and then add them to the model on-demand. Our results show that DAM improves or maintains the effectiveness of bias mitigation, avoids forgetting in a multi-attribute scenario, and maintains on-par task performance.
  arXiv  Detail & Related papers  (2023-02-13T12:39:45Z)
• Dataless Knowledge Fusion by Merging Weights of Language Models [51.8162883997512]
  Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. This creates a barrier to fusing knowledge across individual models to yield a better single model. We propose a dataless knowledge fusion method that merges models in their parameter space.
  arXiv  Detail & Related papers  (2022-12-19T20:46:43Z)
• Optimal Condition Training for Target Source Separation [56.86138859538063]
  We propose a new optimal condition training method for single-channel target source separation. We show that the complementary information carried by the diverse semantic concepts significantly helps to disentangle and isolate sources of interest.
  arXiv  Detail & Related papers  (2022-11-11T00:04:55Z)
• Model ensemble instead of prompt fusion: a sample-specific knowledge transfer method for few-shot prompt tuning [85.55727213502402]
  We focus on improving the few-shot performance of prompt tuning by transferring knowledge from soft prompts of source tasks. We propose Sample-specific Ensemble of Source Models (SESoM) SESoM learns to adjust the contribution of each source model for each target sample separately when ensembling source model outputs.
  arXiv  Detail & Related papers  (2022-10-23T01:33:16Z)
• Unsupervised Multi-source Domain Adaptation Without Access to Source Data [58.551861130011886]
  Unsupervised Domain Adaptation (UDA) aims to learn a predictor model for an unlabeled domain by transferring knowledge from a separate labeled source domain. We propose a novel and efficient algorithm which automatically combines the source models with suitable weights in such a way that it performs at least as good as the best source model.
  arXiv  Detail & Related papers  (2021-04-05T10:45:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.