Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach

• URL: http://arxiv.org/abs/2211.00824v1
• Date: Wed, 2 Nov 2022 02:02:51 GMT
• Title: Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach
• Authors: Kaiwen Yang, Yanchao Sun, Jiahao Su, Fengxiang He, Xinmei Tian, Furong Huang, Tianyi Zhou, Dacheng Tao
• Abstract summary: We show that a prior-free autonomous data augmentation's objective can be derived from a representation learning principle. We then propose a practical surrogate to the objective that can be efficiently optimized and integrated seamlessly into existing methods.
• Score: 95.74102207187545
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data augmentation is a critical contributing factor to the success of deep learning but heavily relies on prior domain knowledge which is not always available. Recent works on automatic data augmentation learn a policy to form a sequence of augmentation operations, which are still pre-defined and restricted to limited options. In this paper, we show that a prior-free autonomous data augmentation's objective can be derived from a representation learning principle that aims to preserve the minimum sufficient information of the labels. Given an example, the objective aims at creating a distant "hard positive example" as the augmentation, while still preserving the original label. We then propose a practical surrogate to the objective that can be optimized efficiently and integrated seamlessly into existing methods for a broad class of machine learning tasks, e.g., supervised, semi-supervised, and noisy-label learning. Unlike previous works, our method does not require training an extra generative model but instead leverages the intermediate layer representations of the end-task model for generating data augmentations. In experiments, we show that our method consistently brings non-trivial improvements to the three aforementioned learning tasks from both efficiency and final performance, either or not combined with strong pre-defined augmentations, e.g., on medical images when domain knowledge is unavailable and the existing augmentation techniques perform poorly. Code is available at: https://github.com/kai-wen-yang/LPA3}{https://github.com/kai-wen-yang/LPA3.

Related papers

• Leveraging Skills from Unlabeled Prior Data for Efficient Online Exploration [54.8229698058649]
  We study how unlabeled prior trajectory data can be leveraged to learn efficient exploration strategies. Our method SUPE (Skills from Unlabeled Prior data for Exploration) demonstrates that a careful combination of these ideas compounds their benefits. We empirically show that SUPE reliably outperforms prior strategies, successfully solving a suite of long-horizon, sparse-reward tasks.
  arXiv  Detail & Related papers  (2024-10-23T17:58:45Z)
• Enhancing Visual Continual Learning with Language-Guided Supervision [76.38481740848434]
  Continual learning aims to empower models to learn new tasks without forgetting previously acquired knowledge. We argue that the scarce semantic information conveyed by the one-hot labels hampers the effective knowledge transfer across tasks. Specifically, we use PLMs to generate semantic targets for each class, which are frozen and serve as supervision signals.
  arXiv  Detail & Related papers  (2024-03-24T12:41:58Z)
• Towards Label-Efficient Incremental Learning: A Survey [42.603603392991715]
  We study incremental learning, where a learner is required to adapt to an incoming stream of data with a varying distribution. We identify three subdivisions, namely semi-, few-shot- and self-supervised learning to reduce labeling efforts.
  arXiv  Detail & Related papers  (2023-02-01T10:24:55Z)
• Learning and Retrieval from Prior Data for Skill-based Imitation Learning [47.59794569496233]
  We develop a skill-based imitation learning framework that extracts temporally extended sensorimotor skills from prior data. We identify several key design choices that significantly improve performance on novel tasks.
  arXiv  Detail & Related papers  (2022-10-20T17:34:59Z)
• Self-Distillation for Further Pre-training of Transformers [83.84227016847096]
  We propose self-distillation as a regularization for a further pre-training stage. We empirically validate the efficacy of self-distillation on a variety of benchmark datasets for image and text classification tasks.
  arXiv  Detail & Related papers  (2022-09-30T02:25:12Z)
• Contrastive Learning for Cross-Domain Open World Recognition [17.660958043781154]
  The ability to evolve is fundamental for any valuable autonomous agent whose knowledge cannot remain limited to that injected by the manufacturer. We show how it learns a feature space perfectly suitable to incrementally include new classes and is able to capture knowledge which generalizes across a variety of visual domains. Our method is endowed with a tailored effective stopping criterion for each learning episode and exploits a novel self-paced thresholding strategy.
  arXiv  Detail & Related papers  (2022-03-17T11:23:53Z)
• TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation [39.94053999359753]
  We present a most simple automatic augmentation baseline, TrivialAugment, that outperforms previous methods almost for free. To us, TrivialAugment's effectiveness is very unexpected. We propose best practices for sustained future progress in automatic augmentation methods.
  arXiv  Detail & Related papers  (2021-03-18T10:48:02Z)
• ProtoDA: Efficient Transfer Learning for Few-Shot Intent Classification [21.933876113300897]
  We adopt an alternative approach by transfer learning on an ensemble of related tasks using prototypical networks under the meta-learning paradigm. Using intent classification as a case study, we demonstrate that increasing variability in training tasks can significantly improve classification performance.
  arXiv  Detail & Related papers  (2021-01-28T00:19:13Z)
• Parrot: Data-Driven Behavioral Priors for Reinforcement Learning [79.32403825036792]
  We propose a method for pre-training behavioral priors that can capture complex input-output relationships observed in successful trials. We show how this learned prior can be used for rapidly learning new tasks without impeding the RL agent's ability to try out novel behaviors.
  arXiv  Detail & Related papers  (2020-11-19T18:47:40Z)
• Incremental Object Detection via Meta-Learning [77.55310507917012]
  We propose a meta-learning approach that learns to reshape model gradients, such that information across incremental tasks is optimally shared. In comparison to existing meta-learning methods, our approach is task-agnostic, allows incremental addition of new-classes and scales to high-capacity models for object detection.
  arXiv  Detail & Related papers  (2020-03-17T13:40:00Z)

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.