Distance Learner: Incorporating Manifold Prior to Model Training

• URL: http://arxiv.org/abs/2207.06888v1
• Date: Thu, 14 Jul 2022 13:07:08 GMT
• Title: Distance Learner: Incorporating Manifold Prior to Model Training
• Authors: Aditya Chetan, Nipun Kwatra
• Abstract summary: We propose a new method, Distance Learner, to incorporate the manifold hypothesis as a prior in modern Deep Neural Networks (DNNs) For classification, Distance Learner chooses the class corresponding to the closest predicted class manifold. We evaluate our method on the task of adversarial robustness, and find that it not only outperforms standard classifier by a large margin, but also performs at par with classifiers trained via state-of-the-art adversarial training.
• Score: 1.6752182911522522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The manifold hypothesis (real world data concentrates near low-dimensional manifolds) is suggested as the principle behind the effectiveness of machine learning algorithms in very high dimensional problems that are common in domains such as vision and speech. Multiple methods have been proposed to explicitly incorporate the manifold hypothesis as a prior in modern Deep Neural Networks (DNNs), with varying success. In this paper, we propose a new method, Distance Learner, to incorporate this prior for DNN-based classifiers. Distance Learner is trained to predict the distance of a point from the underlying manifold of each class, rather than the class label. For classification, Distance Learner then chooses the class corresponding to the closest predicted class manifold. Distance Learner can also identify points as being out of distribution (belonging to neither class), if the distance to the closest manifold is higher than a threshold. We evaluate our method on multiple synthetic datasets and show that Distance Learner learns much more meaningful classification boundaries compared to a standard classifier. We also evaluate our method on the task of adversarial robustness, and find that it not only outperforms standard classifier by a large margin, but also performs at par with classifiers trained via state-of-the-art adversarial training.

Related papers

• 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)
• FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning [21.088762527081883]
  Exemplar-free class-incremental learning (CIL) poses several challenges since it prohibits the rehearsal of data from previous tasks. Recent approaches to incrementally learning the classifier by freezing the feature extractor after the first task have gained much attention. We explore prototypical networks for CIL, which generate new class prototypes using the frozen feature extractor and classify the features based on the Euclidean distance to the prototypes.
  arXiv  Detail & Related papers  (2023-09-25T11:54:33Z)
• Equiangular Basis Vectors [26.520084199562692]
  In deep neural networks, models usually end with a k-way fully connected layer with softmax to handle different classification tasks. We propose Equiangular Basis Vectors (EBVs) for classification tasks. Our EBVs won the first place in the 2022 DIGIX Global AI Challenge.
  arXiv  Detail & Related papers  (2023-03-21T07:08:51Z)
• Constructing Balance from Imbalance for Long-tailed Image Recognition [50.6210415377178]
  The imbalance between majority (head) classes and minority (tail) classes severely skews the data-driven deep neural networks. Previous methods tackle with data imbalance from the viewpoints of data distribution, feature space, and model design. We propose a concise paradigm by progressively adjusting label space and dividing the head classes and tail classes. Our proposed model also provides a feature evaluation method and paves the way for long-tailed feature learning.
  arXiv  Detail & Related papers  (2022-08-04T10:22:24Z)
• CAR: Class-aware Regularizations for Semantic Segmentation [20.947897583427192]
  We propose a universal Class-Aware Regularization (CAR) approach to optimize the intra-class variance and inter-class distance during feature learning. Our method can be easily applied to most existing segmentation models during training, including OCR and CPNet.
  arXiv  Detail & Related papers  (2022-03-14T15:02:48Z)
• Self-Training: A Survey [5.772546394254112]
  Semi-supervised algorithms aim to learn prediction functions from a small set of labeled observations and a large set of unlabeled observations. Among the existing techniques, self-training methods have undoubtedly attracted greater attention in recent years. We present self-training methods for binary and multi-class classification; as well as their variants and two related approaches.
  arXiv  Detail & Related papers  (2022-02-24T11:40:44Z)
• Rethinking Nearest Neighbors for Visual Classification [56.00783095670361]
  k-NN is a lazy learning method that aggregates the distance between the test image and top-k neighbors in a training set. We adopt k-NN with pre-trained visual representations produced by either supervised or self-supervised methods in two steps. Via extensive experiments on a wide range of classification tasks, our study reveals the generality and flexibility of k-NN integration.
  arXiv  Detail & Related papers  (2021-12-15T20:15:01Z)
• OVANet: One-vs-All Network for Universal Domain Adaptation [78.86047802107025]
  Existing methods manually set a threshold to reject unknown samples based on validation or a pre-defined ratio of unknown samples. We propose a method to learn the threshold using source samples and to adapt it to the target domain. Our idea is that a minimum inter-class distance in the source domain should be a good threshold to decide between known or unknown in the target.
  arXiv  Detail & Related papers  (2021-04-07T18:36:31Z)
• Binary Classification from Multiple Unlabeled Datasets via Surrogate Set Classification [94.55805516167369]
  We propose a new approach for binary classification from m U-sets for $mge2$. Our key idea is to consider an auxiliary classification task called surrogate set classification (SSC)
  arXiv  Detail & Related papers  (2021-02-01T07:36:38Z)
• Learning and Evaluating Representations for Deep One-class Classification [59.095144932794646]
  We present a two-stage framework for deep one-class classification. We first learn self-supervised representations from one-class data, and then build one-class classifiers on learned representations. In experiments, we demonstrate state-of-the-art performance on visual domain one-class classification benchmarks.
  arXiv  Detail & Related papers  (2020-11-04T23:33:41Z)
• Beyond cross-entropy: learning highly separable feature distributions for robust and accurate classification [22.806324361016863]
  We propose a novel approach for training deep robust multiclass classifiers that provides adversarial robustness. We show that the regularization of the latent space based on our approach yields excellent classification accuracy.
  arXiv  Detail & Related papers  (2020-10-29T11:15:17Z)

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.