Unsupervised Domain Adaptation with Histogram-gated Image Translation for Delayered IC Image Analysis

• URL: http://arxiv.org/abs/2209.13479v1
• Date: Tue, 27 Sep 2022 15:53:22 GMT
• Title: Unsupervised Domain Adaptation with Histogram-gated Image Translation for Delayered IC Image Analysis
• Authors: Yee-Yang Tee, Deruo Cheng, Chye-Soon Chee, Tong Lin, Yiqiong Shi, Bah-Hwee Gwee
• Abstract summary: Histogram-gated Image Translation (HGIT) is an unsupervised domain adaptation framework which transforms images from a given source dataset to the domain of a target dataset. Our method achieves the best performance compared to the reported domain adaptation techniques, and is also reasonably close to the fully supervised benchmark.
• Score: 2.720699926154399
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has achieved great success in the challenging circuit annotation task by employing Convolutional Neural Networks (CNN) for the segmentation of circuit structures. The deep learning approaches require a large amount of manually annotated training data to achieve a good performance, which could cause a degradation in performance if a deep learning model trained on a given dataset is applied to a different dataset. This is commonly known as the domain shift problem for circuit annotation, which stems from the possibly large variations in distribution across different image datasets. The different image datasets could be obtained from different devices or different layers within a single device. To address the domain shift problem, we propose Histogram-gated Image Translation (HGIT), an unsupervised domain adaptation framework which transforms images from a given source dataset to the domain of a target dataset, and utilize the transformed images for training a segmentation network. Specifically, our HGIT performs generative adversarial network (GAN)-based image translation and utilizes histogram statistics for data curation. Experiments were conducted on a single labeled source dataset adapted to three different target datasets (without labels for training) and the segmentation performance was evaluated for each target dataset. We have demonstrated that our method achieves the best performance compared to the reported domain adaptation techniques, and is also reasonably close to the fully supervised benchmark.

Related papers

• UnSeGArmaNet: Unsupervised Image Segmentation using Graph Neural Networks with Convolutional ARMA Filters [10.940349832919699]
  We propose an unsupervised segmentation framework with a pre-trained ViT. By harnessing the graph structure inherent within the image, the proposed method achieves a notable performance in segmentation. The proposed method provides state-of-the-art performance (even comparable to supervised methods) on benchmark image segmentation datasets.
  arXiv  Detail & Related papers  (2024-10-08T15:10:09Z)
• Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems [80.62854148838359]
  Eye image segmentation is a critical step in eye tracking that has great influence over the final gaze estimate. We use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data. Our methods result in robust, improved performance when tackling the discrepancy between simulation and real-world data samples.
  arXiv  Detail & Related papers  (2024-03-23T22:32:06Z)
• Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers [50.576354045312115]
  Direct image-to-graph transformation is a challenging task that solves object detection and relationship prediction in a single model. We introduce a set of methods enabling cross-domain and cross-dimension transfer learning for image-to-graph transformers. We demonstrate our method's utility in cross-domain and cross-dimension experiments, where we pretrain our models on 2D satellite images before applying them to vastly different target domains in 2D and 3D.
  arXiv  Detail & Related papers  (2024-03-11T10:48:56Z)
• An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset [2.048226951354646]
  We present a method for generating ground-truth disparity maps directly from Light Detection and Ranging (LiDAR) and images. We evaluate 11 dense matching methods across datasets with diverse scene types, image resolutions, and geometric configurations.
  arXiv  Detail & Related papers  (2024-02-19T20:33:46Z)
• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Diversify Your Vision Datasets with Automatic Diffusion-Based Augmentation [66.6546668043249]
  ALIA (Automated Language-guided Image Augmentation) is a method which utilizes large vision and language models to automatically generate natural language descriptions of a dataset's domains. To maintain data integrity, a model trained on the original dataset filters out minimal image edits and those which corrupt class-relevant information. We show that ALIA is able to surpasses traditional data augmentation and text-to-image generated data on fine-grained classification tasks.
  arXiv  Detail & Related papers  (2023-05-25T17:43:05Z)
• CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations [90.50864830038202]
  We present Contrastive Spatial Pre-Training (CSP), a self-supervised learning framework for geo-tagged images. We use a dual-encoder to separately encode the images and their corresponding geo-locations, and use contrastive objectives to learn effective location representations from images. CSP significantly boosts the model performance with 10-34% relative improvement with various labeled training data sampling ratios.
  arXiv  Detail & Related papers  (2023-05-01T23:11:18Z)
• Prefix Conditioning Unifies Language and Label Supervision [84.11127588805138]
  We show that dataset biases negatively affect pre-training by reducing the generalizability of learned representations. In experiments, we show that this simple technique improves the performance in zero-shot image recognition accuracy and robustness to the image-level distribution shift.
  arXiv  Detail & Related papers  (2022-06-02T16:12:26Z)
• HYLDA: End-to-end Hybrid Learning Domain Adaptation for LiDAR Semantic Segmentation [13.87939140266266]
  This paper addresses the problem of training a LiDAR semantic segmentation network using a fully-labeled source dataset and a target dataset that only has a small number of labels. We develop a novel image-to-image translation engine, and couple it with a LiDAR semantic segmentation network, resulting in an integrated domain adaptation architecture we call HYLDA.
  arXiv  Detail & Related papers  (2022-01-14T18:13:09Z)
• Realistic Image Normalization for Multi-Domain Segmentation [7.856339385917824]
  This paper revisits the conventional image normalization approach by instead learning a common normalizing function across multiple datasets. Jointly normalizing multiple datasets is shown to yield consistent normalized images as well as an improved image segmentation. Our method can also enhance data availability by increasing the number of samples available when learning from multiple imaging domains.
  arXiv  Detail & Related papers  (2020-09-29T13:57:04Z)
• Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images [3.786567767772753]
  We propose a segmentation network based on a domain adaptive transfer attack scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Cross-dataset experiments and the ablation study are conducted for the three different datasets.
  arXiv  Detail & Related papers  (2020-04-11T06:17:13Z)

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.