Contextual Interference Reduction by Selective Fine-Tuning of Neural Networks

• URL: http://arxiv.org/abs/2011.10857v1
• Date: Sat, 21 Nov 2020 20:11:12 GMT
• Title: Contextual Interference Reduction by Selective Fine-Tuning of Neural Networks
• Authors: Mahdi Biparva, John Tsotsos
• Abstract summary: We study the role of the context on interfering with a disentangled foreground target object representation. We work on a framework that benefits from the bottom-up and top-down processing paradigms.
• Score: 1.0152838128195465
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Feature disentanglement of the foreground target objects and the background surrounding context has not been yet fully accomplished. The lack of network interpretability prevents advancing for feature disentanglement and better generalization robustness. We study the role of the context on interfering with a disentangled foreground target object representation in this work. We hypothesize that the representation of the surrounding context is heavily tied with the foreground object due to the dense hierarchical parametrization of convolutional networks with under-constrained learning algorithms. Working on a framework that benefits from the bottom-up and top-down processing paradigms, we investigate a systematic approach to shift learned representations in feedforward networks from the emphasis on the irrelevant context to the foreground objects. The top-down processing provides importance maps as the means of the network internal self-interpretation that will guide the learning algorithm to focus on the relevant foreground regions towards achieving a more robust representations. We define an experimental evaluation setup with the role of context emphasized using the MNIST dataset. The experimental results reveal not only that the label prediction accuracy is improved but also a higher degree of robustness to the background perturbation using various noise generation methods is obtained.

Related papers

• Deep Semantic Statistics Matching (D2SM) Denoising Network [70.01091467628068]
  We introduce the Deep Semantic Statistics Matching (D2SM) Denoising Network. It exploits semantic features of pretrained classification networks, then it implicitly matches the probabilistic distribution of clear images at the semantic feature space. By learning to preserve the semantic distribution of denoised images, we empirically find our method significantly improves the denoising capabilities of networks.
  arXiv  Detail & Related papers  (2022-07-19T14:35:42Z)
• USegScene: Unsupervised Learning of Depth, Optical Flow and Ego-Motion with Semantic Guidance and Coupled Networks [31.600708674008384]
  USegScene is a framework for semantically guided unsupervised learning of depth, optical flow and ego-motion estimation for stereo camera images. We present results on the popular KITTI dataset and show that our approach outperforms other methods by a large margin.
  arXiv  Detail & Related papers  (2022-07-15T13:25:47Z)
• CoDo: Contrastive Learning with Downstream Background Invariance for Detection [10.608660802917214]
  We propose a novel object-level self-supervised learning method, called Contrastive learning with Downstream background invariance (CoDo) The pretext task is converted to focus on instance location modeling for various backgrounds, especially for downstream datasets. Experiments on MSCOCO demonstrate that the proposed CoDo with common backbones, ResNet50-FPN, yields strong transfer learning results for object detection.
  arXiv  Detail & Related papers  (2022-05-10T01:26:15Z)
• Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss [25.798361683744684]
  Domain-Agnostic Contrastive Learning (DACL) is a two-stage unsupervised domain adaptation framework with cyclic adversarial training and contrastive loss. Our proposed approach achieves better performance in the monocular depth estimation task compared to previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-03-10T07:06:03Z)
• Variational Structured Attention Networks for Deep Visual Representation Learning [49.80498066480928]
  We propose a unified deep framework to jointly learn both spatial attention maps and channel attention in a principled manner. Specifically, we integrate the estimation and the interaction of the attentions within a probabilistic representation learning framework. We implement the inference rules within the neural network, thus allowing for end-to-end learning of the probabilistic and the CNN front-end parameters.
  arXiv  Detail & Related papers  (2021-03-05T07:37:24Z)
• Context Decoupling Augmentation for Weakly Supervised Semantic Segmentation [53.49821324597837]
  Weakly supervised semantic segmentation is a challenging problem that has been deeply studied in recent years. We present a Context Decoupling Augmentation ( CDA) method to change the inherent context in which the objects appear. To validate the effectiveness of the proposed method, extensive experiments on PASCAL VOC 2012 dataset with several alternative network architectures demonstrate that CDA can boost various popular WSSS methods to the new state-of-the-art by a large margin.
  arXiv  Detail & Related papers  (2021-03-02T15:05:09Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Learning Connectivity of Neural Networks from a Topological Perspective [80.35103711638548]
  We propose a topological perspective to represent a network into a complete graph for analysis. By assigning learnable parameters to the edges which reflect the magnitude of connections, the learning process can be performed in a differentiable manner. This learning process is compatible with existing networks and owns adaptability to larger search spaces and different tasks.
  arXiv  Detail & Related papers  (2020-08-19T04:53:31Z)
• Deep Semantic Matching with Foreground Detection and Cycle-Consistency [103.22976097225457]
  We address weakly supervised semantic matching based on a deep network. We explicitly estimate the foreground regions to suppress the effect of background clutter. We develop cycle-consistent losses to enforce the predicted transformations across multiple images to be geometrically plausible and consistent.
  arXiv  Detail & Related papers  (2020-03-31T22:38:09Z)

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.