Empirical Evaluation of Supervision Signals for Style Transfer Models

• URL: http://arxiv.org/abs/2101.06172v1
• Date: Fri, 15 Jan 2021 15:33:30 GMT
• Title: Empirical Evaluation of Supervision Signals for Style Transfer Models
• Authors: Yevgeniy Puzikov, Simoes Stanley, Iryna Gurevych and Immanuel Schweizer
• Abstract summary: In this work we empirically compare the dominant optimization paradigms which provide supervision signals during training. We find that backtranslation has model-specific limitations, which inhibits training style transfer models. We also experiment with Minimum Risk Training, a popular technique in the machine translation community, which, to our knowledge, has not been empirically evaluated in the task of style transfer.
• Score: 44.39622949370144
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Text style transfer has gained increasing attention from the research community over the recent years. However, the proposed approaches vary in many ways, which makes it hard to assess the individual contribution of the model components. In style transfer, the most important component is the optimization technique used to guide the learning in the absence of parallel training data. In this work we empirically compare the dominant optimization paradigms which provide supervision signals during training: backtranslation, adversarial training and reinforcement learning. We find that backtranslation has model-specific limitations, which inhibits training style transfer models. Reinforcement learning shows the best performance gains, while adversarial training, despite its popularity, does not offer an advantage over the latter alternative. In this work we also experiment with Minimum Risk Training, a popular technique in the machine translation community, which, to our knowledge, has not been empirically evaluated in the task of style transfer. We fill this research gap and empirically show its efficacy.

Related papers

• Towards Understanding the Influence of Reward Margin on Preference Model Performance [8.891183078634786]
  This study introduces a novel method to estimate the preference differences without the need for detailed, exhaustive labels from human annotators. Our experimental results provide empirical evidence that incorporating margin values into the training process significantly improves the effectiveness of reward models.
  arXiv  Detail & Related papers  (2024-04-07T12:10:04Z)
• Fast Propagation is Better: Accelerating Single-Step Adversarial Training via Sampling Subnetworks [69.54774045493227]
  A drawback of adversarial training is the computational overhead introduced by the generation of adversarial examples. We propose to exploit the interior building blocks of the model to improve efficiency. Compared with previous methods, our method not only reduces the training cost but also achieves better model robustness.
  arXiv  Detail & Related papers  (2023-10-24T01:36:20Z)
• Weighted Ensemble Self-Supervised Learning [67.24482854208783]
  Ensembling has proven to be a powerful technique for boosting model performance. We develop a framework that permits data-dependent weighted cross-entropy losses. Our method outperforms both in multiple evaluation metrics on ImageNet-1K.
  arXiv  Detail & Related papers  (2022-11-18T02:00:17Z)
• Enhancing Adversarial Training with Feature Separability [52.39305978984573]
  We introduce a new concept of adversarial training graph (ATG) with which the proposed adversarial training with feature separability (ATFS) enables to boost the intra-class feature similarity and increase inter-class feature variance. Through comprehensive experiments, we demonstrate that the proposed ATFS framework significantly improves both clean and robust performance.
  arXiv  Detail & Related papers  (2022-05-02T04:04:23Z)
• Imitation Learning by State-Only Distribution Matching [2.580765958706854]
  Imitation Learning from observation describes policy learning in a similar way to human learning. We propose a non-adversarial learning-from-observations approach, together with an interpretable convergence and performance metric.
  arXiv  Detail & Related papers  (2022-02-09T08:38:50Z)
• Adversarial Training Helps Transfer Learning via Better Representations [17.497590668804055]
  Transfer learning aims to leverage models pre-trained on source data to efficiently adapt to target setting. Recent works empirically demonstrate that adversarial training in the source data can improve the ability of models to transfer to new domains. We show that adversarial training in the source data generates provably better representations, so fine-tuning on top of this representation leads to a more accurate predictor of the target data.
  arXiv  Detail & Related papers  (2021-06-18T15:41:07Z)
• Delving into Data: Effectively Substitute Training for Black-box Attack [84.85798059317963]
  We propose a novel perspective substitute training that focuses on designing the distribution of data used in the knowledge stealing process. The combination of these two modules can further boost the consistency of the substitute model and target model, which greatly improves the effectiveness of adversarial attack.
  arXiv  Detail & Related papers  (2021-04-26T07:26:29Z)
• TransMIA: Membership Inference Attacks Using Transfer Shadow Training [5.22523722171238]
  We propose TransMIA (Transfer learning-based Membership Inference Attacks), which use transfer learning to perform membership inference attacks on the source model. In particular, we propose a transfer shadow training technique, where an adversary employs the parameters of the transferred model to construct shadow models. We evaluate our attacks using two real datasets, and show that our attacks outperform the state-of-the-art that does not use our transfer shadow training technique.
  arXiv  Detail & Related papers  (2020-11-30T10:03:43Z)
• On Learning Text Style Transfer with Direct Rewards [101.97136885111037]
  Lack of parallel corpora makes it impossible to directly train supervised models for the text style transfer task. We leverage semantic similarity metrics originally used for fine-tuning neural machine translation models. Our model provides significant gains in both automatic and human evaluation over strong baselines.
  arXiv  Detail & Related papers  (2020-10-24T04:30:02Z)

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.