Flexible deep transfer learning by separate feature embeddings and manifold alignment

• URL: http://arxiv.org/abs/2012.12302v1
• Date: Tue, 22 Dec 2020 19:24:44 GMT
• Title: Flexible deep transfer learning by separate feature embeddings and manifold alignment
• Authors: Samuel Rivera, Joel Klipfel, Deborah Weeks
• Abstract summary: Object recognition is a key enabler across industry and defense. Unfortunately, algorithms trained on existing labeled datasets do not directly generalize to new data because the data distributions do not match. We propose a novel deep learning framework that overcomes this limitation by learning separate feature extractions for each domain.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object recognition is a key enabler across industry and defense. As technology changes, algorithms must keep pace with new requirements and data. New modalities and higher resolution sensors should allow for increased algorithm robustness. Unfortunately, algorithms trained on existing labeled datasets do not directly generalize to new data because the data distributions do not match. Transfer learning (TL) or domain adaptation (DA) methods have established the groundwork for transferring knowledge from existing labeled source data to new unlabeled target datasets. However, current DA approaches assume similar source and target feature spaces and suffer in the case of massive domain shifts or changes in the feature space. Existing methods assume the data are either the same modality, or can be aligned to a common feature space. Therefore, most methods are not designed to support a fundamental domain change such as visual to auditory data. We propose a novel deep learning framework that overcomes this limitation by learning separate feature extractions for each domain while minimizing the distance between the domains in a latent lower-dimensional space. The alignment is achieved by considering the data manifold along with an adversarial training procedure. We demonstrate the effectiveness of the approach versus traditional methods with several ablation experiments on synthetic, measured, and satellite image datasets. We also provide practical guidelines for training the network while overcoming vanishing gradients which inhibit learning in some adversarial training settings.

Related papers

• FedILC: Weighted Geometric Mean and Invariant Gradient Covariance for Federated Learning on Non-IID Data [69.0785021613868]
  Federated learning is a distributed machine learning approach which enables a shared server model to learn by aggregating the locally-computed parameter updates with the training data from spatially-distributed client silos. We propose the Federated Invariant Learning Consistency (FedILC) approach, which leverages the gradient covariance and the geometric mean of Hessians to capture both inter-silo and intra-silo consistencies. This is relevant to various fields such as medical healthcare, computer vision, and the Internet of Things (IoT)
  arXiv  Detail & Related papers  (2022-05-19T03:32:03Z)
• Bi-level Alignment for Cross-Domain Crowd Counting [113.78303285148041]
  Current methods rely on external data for training an auxiliary task or apply an expensive coarse-to-fine estimation. We develop a new adversarial learning based method, which is simple and efficient to apply. We evaluate our approach on five real-world crowd counting benchmarks, where we outperform existing approaches by a large margin.
  arXiv  Detail & Related papers  (2022-05-12T02:23:25Z)
• On Generalizing Beyond Domains in Cross-Domain Continual Learning [91.56748415975683]
  Deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Our proposed approach learns new tasks under domain shift with accuracy boosts up to 10% on challenging datasets such as DomainNet and OfficeHome.
  arXiv  Detail & Related papers  (2022-03-08T09:57:48Z)
• Ranking Distance Calibration for Cross-Domain Few-Shot Learning [91.22458739205766]
  Recent progress in few-shot learning promotes a more realistic cross-domain setting. Due to the domain gap and disjoint label spaces between source and target datasets, their shared knowledge is extremely limited. We employ a re-ranking process for calibrating a target distance matrix by discovering the reciprocal k-nearest neighbours within the task.
  arXiv  Detail & Related papers  (2021-12-01T03:36:58Z)
• Domain Adaptation by Topology Regularization [0.0]
  Domain adaptation (DA) or transfer learning (TL) enables algorithms to transfer knowledge from a labelled (source) data set to an unlabelled but related (target) data set of interest. We propose to leverage global data structure by applying a topological data analysis technique called persistent homology to TL.
  arXiv  Detail & Related papers  (2021-01-28T16:45:41Z)
• Data-efficient Weakly-supervised Learning for On-line Object Detection under Domain Shift in Robotics [24.878465999976594]
  Several object detection methods have been proposed in the literature, the vast majority based on Deep Convolutional Neural Networks (DCNNs) These methods have important limitations for robotics: Learning solely on off-line data may introduce biases, and prevents adaptation to novel tasks. In this work, we investigate how weakly-supervised learning can cope with these problems.
  arXiv  Detail & Related papers  (2020-12-28T16:36:11Z)
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
  arXiv  Detail & Related papers  (2020-09-01T00:06:50Z)
• Understanding Self-Training for Gradual Domain Adaptation [107.37869221297687]
  We consider gradual domain adaptation, where the goal is to adapt an initial classifier trained on a source domain given only unlabeled data that shifts gradually in distribution towards a target domain. We prove the first non-vacuous upper bound on the error of self-training with gradual shifts, under settings where directly adapting to the target domain can result in unbounded error. The theoretical analysis leads to algorithmic insights, highlighting that regularization and label sharpening are essential even when we have infinite data, and suggesting that self-training works particularly well for shifts with small Wasserstein-infinity distance.
  arXiv  Detail & Related papers  (2020-02-26T08:59:40Z)

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.