Training-Free Model Merging for Multi-target Domain Adaptation

• URL: http://arxiv.org/abs/2407.13771v1
• Date: Thu, 18 Jul 2024 17:59:57 GMT
• Title: Training-Free Model Merging for Multi-target Domain Adaptation
• Authors: Wenyi Li, Huan-ang Gao, Mingju Gao, Beiwen Tian, Rong Zhi, Hao Zhao,
• Abstract summary: We study multi-target domain adaptation of scene understanding models. Our solution involves two components, merging model parameters and merging model buffers. Our method is simple yet effective, achieving comparable performance with data combination training baselines.
• Score: 6.00960357022946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we study multi-target domain adaptation of scene understanding models. While previous methods achieved commendable results through inter-domain consistency losses, they often assumed unrealistic simultaneous access to images from all target domains, overlooking constraints such as data transfer bandwidth limitations and data privacy concerns. Given these challenges, we pose the question: How to merge models adapted independently on distinct domains while bypassing the need for direct access to training data? Our solution to this problem involves two components, merging model parameters and merging model buffers (i.e., normalization layer statistics). For merging model parameters, empirical analyses of mode connectivity surprisingly reveal that linear merging suffices when employing the same pretrained backbone weights for adapting separate models. For merging model buffers, we model the real-world distribution with a Gaussian prior and estimate new statistics from the buffers of separately trained models. Our method is simple yet effective, achieving comparable performance with data combination training baselines, while eliminating the need for accessing training data. Project page: https://air-discover.github.io/ModelMerging

Related papers

• Self-calibration for Language Model Quantization and Pruning [38.00221764773372]
  Quantization and pruning are fundamental approaches for model compression. In a post-training setting, state-of-the-art quantization and pruning methods require calibration data. We propose self-calibration as a solution.
  arXiv  Detail & Related papers  (2024-10-22T16:50:00Z)
• Truncated Consistency Models [57.50243901368328]
  Training consistency models requires learning to map all intermediate points along PF ODE trajectories to their corresponding endpoints. We empirically find that this training paradigm limits the one-step generation performance of consistency models. We propose a new parameterization of the consistency function and a two-stage training procedure that prevents the truncated-time training from collapsing to a trivial solution.
  arXiv  Detail & Related papers  (2024-10-18T22:38:08Z)
• Harmony in Diversity: Merging Neural Networks with Canonical Correlation Analysis [17.989809995141044]
  We propose CCA Merge, which is based on Corre Analysis Analysis. We show that CCA works significantly better than past methods when more than 2 models are merged.
  arXiv  Detail & Related papers  (2024-07-07T14:21:04Z)
• PLeaS -- Merging Models with Permutations and Least Squares [43.17620198572947]
  We propose a new two-step algorithm to merge models-termed PLeaS. PLeaS partially matches nodes in each layer by maximizing alignment. It computes the weights of the merged model as a layer-wise Least Squares solution.
  arXiv  Detail & Related papers  (2024-07-02T17:24:04Z)
• 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)
• Learning from aggregated data with a maximum entropy model [73.63512438583375]
  We show how a new model, similar to a logistic regression, may be learned from aggregated data only by approximating the unobserved feature distribution with a maximum entropy hypothesis. We present empirical evidence on several public datasets that the model learned this way can achieve performances comparable to those of a logistic model trained with the full unaggregated data.
  arXiv  Detail & Related papers  (2022-10-05T09:17:27Z)
• On-the-Fly Test-time Adaptation for Medical Image Segmentation [63.476899335138164]
  Adapting the source model to target data distribution at test-time is an efficient solution for the data-shift problem. We propose a new framework called Adaptive UNet where each convolutional block is equipped with an adaptive batch normalization layer. During test-time, the model takes in just the new test image and generates a domain code to adapt the features of source model according to the test data.
  arXiv  Detail & Related papers  (2022-03-10T18:51:29Z)
• GAN Cocktail: mixing GANs without dataset access [18.664733153082146]
  We tackle the problem of model merging, given two constraints that often come up in the real world. In the first stage, we transform the weights of all the models to the same parameter space by a technique we term model rooting. In the second stage, we merge the rooted models by averaging their weights and fine-tuning them for each specific domain, using only data generated by the original trained models.
  arXiv  Detail & Related papers  (2021-06-07T17:59:04Z)
• Variational Bayesian Unlearning [54.26984662139516]
  We study the problem of approximately unlearning a Bayesian model from a small subset of the training data to be erased. We show that it is equivalent to minimizing an evidence upper bound which trades off between fully unlearning from erased data vs. not entirely forgetting the posterior belief. In model training with VI, only an approximate (instead of exact) posterior belief given the full data can be obtained, which makes unlearning even more challenging.
  arXiv  Detail & Related papers  (2020-10-24T11:53:00Z)
• Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision [73.76277367528657]
  Convolutional neural network-based approaches have achieved remarkable progress in semantic segmentation. To cope with this limitation, automatically annotated data generated from graphic engines are used to train segmentation models. We propose a two-step self-supervised domain adaptation approach to minimize the inter-domain and intra-domain gap together.
  arXiv  Detail & Related papers  (2020-04-16T15:24:11Z)

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.