10 research outputs found
Class reconstruction driven adversarial domain adaptation for hyperspectral image classification
We address the problem of cross-domain classification of hyperspectral image (HSI) pairs under the notion of unsupervised domain adaptation (UDA). The UDA problem aims at classifying the test samples of a target domain by exploiting the labeled training samples from a related but different source domain. In this respect, the use of adversarial training driven domain classifiers is popular which seeks to learn a shared feature space for both the domains. However, such a formalism apparently fails to ensure the (i) discriminativeness, and (ii) non-redundancy of the learned space. In general, the feature space learned by domain classifier does not convey any meaningful insight regarding the data. On the other hand, we are interested in constraining the space which is deemed to be simultaneously discriminative and reconstructive at the class-scale. In particular, the reconstructive constraint enables the learning of category-specific meaningful feature abstractions and UDA in such a latent space is expected to better associate the domains. On the other hand, we consider an orthogonality constraint to ensure non-redundancy of the learned space. Experimental results obtained on benchmark HSI datasets (Botswana and Pavia) confirm the efficacy of the proposal approach
Information Theory-Guided Heuristic Progressive Multi-View Coding
Multi-view representation learning aims to capture comprehensive information
from multiple views of a shared context. Recent works intuitively apply
contrastive learning to different views in a pairwise manner, which is still
scalable: view-specific noise is not filtered in learning view-shared
representations; the fake negative pairs, where the negative terms are actually
within the same class as the positive, and the real negative pairs are
coequally treated; evenly measuring the similarities between terms might
interfere with optimization. Importantly, few works study the theoretical
framework of generalized self-supervised multi-view learning, especially for
more than two views. To this end, we rethink the existing multi-view learning
paradigm from the perspective of information theory and then propose a novel
information theoretical framework for generalized multi-view learning. Guided
by it, we build a multi-view coding method with a three-tier progressive
architecture, namely Information theory-guided hierarchical Progressive
Multi-view Coding (IPMC). In the distribution-tier, IPMC aligns the
distribution between views to reduce view-specific noise. In the set-tier, IPMC
constructs self-adjusted contrasting pools, which are adaptively modified by a
view filter. Lastly, in the instance-tier, we adopt a designed unified loss to
learn representations and reduce the gradient interference. Theoretically and
empirically, we demonstrate the superiority of IPMC over state-of-the-art
methods.Comment: This paper is accepted by the jourcal of Neural Networks (Elsevier)
by 2023. A revised manuscript of arXiv:2109.0234
False textual information detection, a deep learning approach
Many approaches exist for analysing fact checking for fake news identification, which is the focus of this thesis. Current approaches still perform badly on a large scale due to a lack of authority, or insufficient evidence, or in certain cases reliance on a single piece of evidence.
To address the lack of evidence and the inability of models to generalise across domains, we propose a style-aware model for detecting false information and improving existing performance. We discovered that our model was effective at detecting false information when we evaluated its generalisation ability using news articles and Twitter corpora.
We then propose to improve fact checking performance by incorporating warrants. We developed a highly efficient prediction model based on the results and demonstrated that incorporating is beneficial for fact checking. Due to a lack of external warrant data, we develop a novel model for generating warrants that aid in determining the credibility of a claim. The results indicate that when a pre-trained language model is combined with a multi-agent model, high-quality, diverse warrants are generated that contribute to task performance improvement.
To resolve a biased opinion and making rational judgments, we propose a model that can generate multiple perspectives on the claim. Experiments confirm that our Perspectives Generation model allows for the generation of diverse perspectives with a higher degree of quality and diversity than any other baseline model.
Additionally, we propose to improve the model's detection capability by generating an explainable alternative factual claim assisting the reader in identifying subtle issues that result in factual errors. The examination demonstrates that it does indeed increase the veracity of the claim.
Finally, current research has focused on stance detection and fact checking separately, we propose a unified model that integrates both tasks. Classification results demonstrate that our proposed model outperforms state-of-the-art methods