mask-Net: Learning Context Aware Invariant Features using Adversarial Forgetting (Student Abstract)

• URL: http://arxiv.org/abs/2011.12979v5
• Date: Mon, 18 Oct 2021 12:56:38 GMT
• Title: mask-Net: Learning Context Aware Invariant Features using Adversarial Forgetting (Student Abstract)
• Authors: Hemant Yadav, Atul Anshuman Singh, Rachit Mittal, Sunayana Sitaram, Yi Yu, Rajiv Ratn Shah
• Abstract summary: We propose a novel approach to induce invariance using adversarial forgetting (AF) Our initial experiments on learning invariant features such as accent on the STT task achieve better generalizations in terms of word error rate (WER) compared to the traditional models.
• Score: 46.61843360106884
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Training a robust system, e.g.,Speech to Text (STT), requires large datasets. Variability present in the dataset such as unwanted nuisances and biases are the reason for the need of large datasets to learn general representations. In this work, we propose a novel approach to induce invariance using adversarial forgetting (AF). Our initial experiments on learning invariant features such as accent on the STT task achieve better generalizations in terms of word error rate (WER) compared to the traditional models. We observe an absolute improvement of 2.2% and 1.3% on out-of-distribution and in-distribution test sets, respectively.

Related papers

• Improving General Text Embedding Model: Tackling Task Conflict and Data Imbalance through Model Merging [33.23758947497205]
  Advanced embedding models are typically developed using large-scale multi-task data and joint training across multiple tasks. To overcome these challenges, we explore model merging-a technique that combines independently trained models to mitigate gradient conflicts and balance data distribution. We introduce a novel method, Self Positioning, which efficiently searches for optimal model combinations within the space of task vectors using gradient descent.
  arXiv  Detail & Related papers  (2024-10-19T08:39:21Z)
• Uncertainty Aware Learning for Language Model Alignment [97.36361196793929]
  We propose uncertainty-aware learning (UAL) to improve the model alignment of different task scenarios. We implement UAL in a simple fashion -- adaptively setting the label smoothing value of training according to the uncertainty of individual samples. Experiments on widely used benchmarks demonstrate that our UAL significantly and consistently outperforms standard supervised fine-tuning.
  arXiv  Detail & Related papers  (2024-06-07T11:37:45Z)
• The Trade-off between Universality and Label Efficiency of Representations from Contrastive Learning [32.15608637930748]
  We show that there exists a trade-off between the two desiderata so that one may not be able to achieve both simultaneously. We provide analysis using a theoretical data model and show that, while more diverse pre-training data result in more diverse features for different tasks, it puts less emphasis on task-specific features.
  arXiv  Detail & Related papers  (2023-02-28T22:14:33Z)
• Gaussian Switch Sampling: A Second Order Approach to Active Learning [11.775252660867285]
  In active learning, acquisition functions define informativeness directly on the representation position within the model manifold. We propose a grounded second-order definition of information content and sample importance within the context of active learning. We show that our definition produces highly accurate importance scores even when the model representations are constrained by the lack of training data.
  arXiv  Detail & Related papers  (2023-02-16T15:24:56Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning [55.733193075728096]
  Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. We propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data.
  arXiv  Detail & Related papers  (2022-02-11T13:49:51Z)
• Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification [17.237331828747006]
  This work focuses on few-shot relation classification (FSRC) We propose an adaptive mixture mechanism to add label words to the representation of the class prototype. Experiments have been conducted on FewRel under different few-shot (FS) settings.
  arXiv  Detail & Related papers  (2021-01-10T11:25:42Z)
• Squared $\ell_2$ Norm as Consistency Loss for Leveraging Augmented Data to Learn Robust and Invariant Representations [76.85274970052762]
  Regularizing distance between embeddings/representations of original samples and augmented counterparts is a popular technique for improving robustness of neural networks. In this paper, we explore these various regularization choices, seeking to provide a general understanding of how we should regularize the embeddings. We show that the generic approach we identified (squared $ell$ regularized augmentation) outperforms several recent methods, which are each specially designed for one task.
  arXiv  Detail & Related papers  (2020-11-25T22:40:09Z)
• Supervised Contrastive Learning for Pre-trained Language Model Fine-tuning [23.00300794016583]
  State-of-the-art natural language understanding classification models follow two-stages. We propose a supervised contrastive learning (SCL) objective for the fine-tuning stage. Our proposed fine-tuning objective leads to models that are more robust to different levels of noise in the fine-tuning training data.
  arXiv  Detail & Related papers  (2020-11-03T01:10:39Z)
• Deep F-measure Maximization for End-to-End Speech Understanding [52.36496114728355]
  We propose a differentiable approximation to the F-measure and train the network with this objective using standard backpropagation. We perform experiments on two standard fairness datasets, Adult, Communities and Crime, and also on speech-to-intent detection on the ATIS dataset and speech-to-image concept classification on the Speech-COCO dataset. In all four of these tasks, F-measure results in improved micro-F1 scores, with absolute improvements of up to 8% absolute, as compared to models trained with the cross-entropy loss function.
  arXiv  Detail & Related papers  (2020-08-08T03:02:27Z)

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.