GA-DAN models cross-domain shifts in geometry and appearance spaces with multi-modal spatial learning and disentangled cycle-consistency loss, yielding superior scene text detection and recognition performance on adapted images.
arXiv preprint arXiv:1606.00704 , year=
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
We introduce the adversarially learned inference (ALI) model, which jointly learns a generation network and an inference network using an adversarial process. The generation network maps samples from stochastic latent variables to the data space while the inference network maps training examples in data space to the space of latent variables. An adversarial game is cast between these two networks and a discriminative network is trained to distinguish between joint latent/data-space samples from the generative network and joint samples from the inference network. We illustrate the ability of the model to learn mutually coherent inference and generation networks through the inspections of model samples and reconstructions and confirm the usefulness of the learned representations by obtaining a performance competitive with state-of-the-art on the semi-supervised SVHN and CIFAR10 tasks.
representative citing papers
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citing papers explorer
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GA-DAN: Geometry-Aware Domain Adaptation Network for Scene Text Detection and Recognition
GA-DAN models cross-domain shifts in geometry and appearance spaces with multi-modal spatial learning and disentangled cycle-consistency loss, yielding superior scene text detection and recognition performance on adapted images.
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Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive growing stabilizes GAN training to produce high-resolution images of unprecedented quality and achieves a record unsupervised inception score of 8.80 on CIFAR10.
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MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework
MetaGPT embeds human SOPs into LLM prompts to create role-specialized agent teams that produce more coherent solutions on collaborative software engineering tasks than prior chat-based multi-agent systems.
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Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision
Two data selection techniques (GMM visual similarity and bounding-box diversity) reduce required weakly labeled images by up to 100x on Open Images and 20x on Cityscapes while maintaining semantic segmentation performance.
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AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data
AMAD is an end-to-end model using adversarial autoencoders and RNNs with attention for multiscale anomaly detection on time-evolving high-dimensional categorical data.
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Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching
MMI-ALI extends pairwise ALI models into an m-domain ensemble by maximizing MMI on joint variables to achieve scalable joint distribution matching with linear scaling in m.
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Information theoretic underpinning of self-supervised learning by clustering
SSL clustering is derived as KL-divergence optimization where a teacher-distribution constraint normalizes via inverse cluster priors and simplifies to batch centering by Jensen's inequality.