Semi-supervised Classification using Attention-based Regularization on
Coarse-resolution Data
- URL: http://arxiv.org/abs/2001.00994v1
- Date: Fri, 3 Jan 2020 21:29:26 GMT
- Title: Semi-supervised Classification using Attention-based Regularization on
Coarse-resolution Data
- Authors: Guruprasad Nayak, Rahul Ghosh, Xiaowei Jia, Varun Mithal, Vipin Kumar
- Abstract summary: We propose classification algorithms that leverage supervision from coarser resolutions to help train models on finer resolutions.
The different resolutions are modeled as different views of the data in a multi-view framework.
Experiments on the real-world application of mapping urban areas using satellite observations and sentiment classification on text data show the effectiveness of the proposed methods.
- Score: 6.4466362693513055
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Many real-world phenomena are observed at multiple resolutions. Predictive
models designed to predict these phenomena typically consider different
resolutions separately. This approach might be limiting in applications where
predictions are desired at fine resolutions but available training data is
scarce. In this paper, we propose classification algorithms that leverage
supervision from coarser resolutions to help train models on finer resolutions.
The different resolutions are modeled as different views of the data in a
multi-view framework that exploits the complementarity of features across
different views to improve models on both views. Unlike traditional multi-view
learning problems, the key challenge in our case is that there is no one-to-one
correspondence between instances across different views in our case, which
requires explicit modeling of the correspondence of instances across
resolutions. We propose to use the features of instances at different
resolutions to learn the correspondence between instances across resolutions
using an attention mechanism.Experiments on the real-world application of
mapping urban areas using satellite observations and sentiment classification
on text data show the effectiveness of the proposed methods.
Related papers
- Cross-Domain Knowledge Distillation for Low-Resolution Human Pose Estimation [31.970739018426645]
In practical applications of human pose estimation, low-resolution inputs frequently occur, and existing state-of-the-art models perform poorly with low-resolution images.
This work focuses on boosting the performance of low-resolution models by distilling knowledge from a high-resolution model.
arXiv Detail & Related papers (2024-05-19T04:57:17Z) - Learning Resolution-Adaptive Representations for Cross-Resolution Person
Re-Identification [49.57112924976762]
Cross-resolution person re-identification problem aims to match low-resolution (LR) query identity images against high resolution (HR) gallery images.
It is a challenging and practical problem since the query images often suffer from resolution degradation due to the different capturing conditions from real-world cameras.
This paper explores an alternative SR-free paradigm to directly compare HR and LR images via a dynamic metric, which is adaptive to the resolution of a query image.
arXiv Detail & Related papers (2022-07-09T03:49:51Z) - Switchable Representation Learning Framework with Self-compatibility [50.48336074436792]
We propose a Switchable representation learning Framework with Self-Compatibility (SFSC)
SFSC generates a series of compatible sub-models with different capacities through one training process.
SFSC achieves state-of-the-art performance on the evaluated datasets.
arXiv Detail & Related papers (2022-06-16T16:46:32Z) - Discriminative Multimodal Learning via Conditional Priors in Generative
Models [21.166519800652047]
This research studies the realistic scenario in which all modalities and class labels are available for model training.
We show, in this scenario, that the variational lower bound limits mutual information between joint representations and missing modalities.
arXiv Detail & Related papers (2021-10-09T17:22:24Z) - Deep Co-Attention Network for Multi-View Subspace Learning [73.3450258002607]
We propose a deep co-attention network for multi-view subspace learning.
It aims to extract both the common information and the complementary information in an adversarial setting.
In particular, it uses a novel cross reconstruction loss and leverages the label information to guide the construction of the latent representation.
arXiv Detail & Related papers (2021-02-15T18:46:44Z) - Scale-Localized Abstract Reasoning [79.00011351374869]
We consider the abstract relational reasoning task, which is commonly used as an intelligence test.
Since some patterns have spatial rationales, while others are only semantic, we propose a multi-scale architecture that processes each query in multiple resolutions.
We show that indeed different rules are solved by different resolutions and a combined multi-scale approach outperforms the existing state of the art in this task on all benchmarks by 5-54%.
arXiv Detail & Related papers (2020-09-20T10:37:29Z) - Unified Representation Learning for Cross Model Compatibility [19.808287296481208]
We propose a unified representation learning framework to address the Cross Model Compatibility problem in the context of visual search applications.
Cross compatibility between different embedding models enables the visual search systems to correctly recognize and retrieve identities without re-encoding user images.
arXiv Detail & Related papers (2020-08-11T16:14:53Z) - Variational Inference for Deep Probabilistic Canonical Correlation
Analysis [49.36636239154184]
We propose a deep probabilistic multi-view model that is composed of a linear multi-view layer and deep generative networks as observation models.
An efficient variational inference procedure is developed that approximates the posterior distributions of the latent probabilistic multi-view layer.
A generalization to models with arbitrary number of views is also proposed.
arXiv Detail & Related papers (2020-03-09T17:51:15Z) - Cross-Resolution Adversarial Dual Network for Person Re-Identification
and Beyond [59.149653740463435]
Person re-identification (re-ID) aims at matching images of the same person across camera views.
Due to varying distances between cameras and persons of interest, resolution mismatch can be expected.
We propose a novel generative adversarial network to address cross-resolution person re-ID.
arXiv Detail & Related papers (2020-02-19T07:21:38Z)
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.