Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement

• URL: http://arxiv.org/abs/2010.08532v2
• Date: Fri, 25 Feb 2022 11:56:12 GMT
• Title: Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement
• Authors: Xingjian Li, Di Hu, Xuhong Li, Haoyi Xiong, Zhi Ye, Zhipeng Wang, Chengzhong Xu, Dejing Dou
• Abstract summary: We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
• Score: 56.40587594647692
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fine-tuning deep neural networks pre-trained on large scale datasets is one of the most practical transfer learning paradigm given limited quantity of training samples. To obtain better generalization, using the starting point as the reference (SPAR), either through weights or features, has been successfully applied to transfer learning as a regularizer. However, due to the domain discrepancy between the source and target task, there exists obvious risk of negative transfer in a straightforward manner of knowledge preserving. In this paper, we propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED), where the relevant knowledge with respect to the target task is disentangled from the original source model and used as a regularizer during fine-tuning the target model. Specifically, we design two alternative methods, maximizing the Maximum Mean Discrepancy (Max-MMD) and minimizing the mutual information (Min-MI), for the representation disentanglement. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average. TRED also outperforms related state-of-the-art transfer learning regularizers such as L2-SP, AT, DELTA, and BSS.

Related papers

• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Knowledge Transfer-Driven Few-Shot Class-Incremental Learning [23.163459923345556]
  Few-shot class-incremental learning (FSCIL) aims to continually learn new classes using a few samples while not forgetting the old classes. Despite the advance of existing FSCIL methods, the proposed knowledge transfer learning schemes are sub-optimal due to the insufficient optimization for the model's plasticity. We propose a Random Episode Sampling and Augmentation (RESA) strategy that relies on diverse pseudo incremental tasks as agents to achieve the knowledge transfer.
  arXiv  Detail & Related papers  (2023-06-19T14:02:45Z)
• Towards Estimating Transferability using Hard Subsets [25.86053764521497]
  We propose HASTE, a new strategy to estimate the transferability of a source model to a particular target task using only a harder subset of target data. We show that HASTE can be used with any existing transferability metric to improve their reliability. Our experimental results across multiple source model architectures, target datasets, and transfer learning tasks show that HASTE modified metrics are consistently better or on par with the state of the art transferability metrics.
  arXiv  Detail & Related papers  (2023-01-17T14:50:18Z)
• Model-Agnostic Multitask Fine-tuning for Few-shot Vision-Language Transfer Learning [59.38343286807997]
  We propose Model-Agnostic Multitask Fine-tuning (MAMF) for vision-language models on unseen tasks. Compared with model-agnostic meta-learning (MAML), MAMF discards the bi-level optimization and uses only first-order gradients. We show that MAMF consistently outperforms the classical fine-tuning method for few-shot transfer learning on five benchmark datasets.
  arXiv  Detail & Related papers  (2022-03-09T17:26:53Z)
• Auto-Transfer: Learning to Route Transferrable Representations [77.30427535329571]
  We propose a novel adversarial multi-armed bandit approach which automatically learns to route source representations to appropriate target representations. We see upwards of 5% accuracy improvements compared with the state-of-the-art knowledge transfer methods.
  arXiv  Detail & Related papers  (2022-02-02T13:09:27Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression [26.5147705530439]
  We define a transfer learning approach to the target task as a linear regression optimization with a regularization on the distance between the to-be-learned target parameters and the already-learned source parameters. We show that for sufficiently related tasks, the optimally tuned transfer learning approach can outperform the optimally tuned ridge regression method.
  arXiv  Detail & Related papers  (2021-03-09T18:46:01Z)
• Meta-learning Transferable Representations with a Single Target Domain [46.83481356352768]
  Fine-tuning and joint training do not always improve accuracy on downstream tasks. We propose Meta Representation Learning (MeRLin) to learn transferable features. MeRLin empirically outperforms previous state-of-the-art transfer learning algorithms on various real-world vision and NLP transfer learning benchmarks.
  arXiv  Detail & Related papers  (2020-11-03T01:57:37Z)
• Uniform Priors for Data-Efficient Transfer [65.086680950871]
  We show that features that are most transferable have high uniformity in the embedding space. We evaluate the regularization on its ability to facilitate adaptation to unseen tasks and data.
  arXiv  Detail & Related papers  (2020-06-30T04:39:36Z)
• Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks [27.44348371795822]
  We develop a statistical minimax framework to characterize the limits of transfer learning. We derive a lower-bound for the target generalization error achievable by any algorithm as a function of the number of labeled source and target data.
  arXiv  Detail & Related papers  (2020-06-16T22:49:26Z)

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.