As a result, it raises concerns about the overall robustness of the machine learning techniques for computer vision applications that are deployed publicly for consumers.
While unsupervised domain adaptation has been explored to leverage the knowledge from a labeled source domain to an unlabeled target domain, existing methods focus on the distribution alignment between two domains.
The goal of unbiased learning to rank (ULTR) is to leverage implicit user feedback for optimizing learning-to-rank systems.
Existing methods in unbiased learning to rank typically rely on click modeling or inverse propensity weighting (IPW).
In this paper, we propose StruBERT, a structure-aware BERT model that fuses the textual and structural information of a data table to produce context-aware representations for both textual and tabular content of a data table.
Ideally, a robust classifier would be immune to small variations in input images, and a number of defensive approaches have been created as a result.
Unsupervised domain adaptation leverages rich information from a labeled source domain to model an unlabeled target domain.
To further improve segmentation results, we are the first to propose a post-processing layer to remove irrelevant portions in the segmentation result.
To address these issues, we propose a novel approach called correlated adversarial joint discrepancy adaptation network (CAJNet), which minimizes the joint discrepancy of two domains and achieves competitive performance with tuning parameters using the correlated label.
To align the conditional distributions, we further develop an easy-to-hard pseudo label refinement process to improve the quality of the pseudo labels and then reduce categorical spherical manifold Gaussian kernel geodesic loss.
We describe the development, characteristics and availability of a test collection for the task of Web table retrieval, which uses a large-scale Web Table Corpora extracted from the Common Crawl.
In this paper, we show how to efficiently opt for the best pre-trained features from seventeen well-known ImageNet models in unsupervised DA problems.
A variety of deep learning models have been proposed, and each model presents a set of neural network components to extract features that are used for ranking.
Adversarial learning loss can maintain domain-invariant features between the source and target domains.
Extreme multi-label text classification (XMTC) is a task for tagging a given text with the most relevant labels from an extremely large label set.
Pretrained contextualized language models such as BERT have achieved impressive results on various natural language processing benchmarks.
We extract features from sixteen distinct pre-trained ImageNet models and examine the performance of twelve benchmarking methods when using the features.
We incorporate the generated schema labels into a mixed ranking model which not only considers the relevance between the query and dataset metadata but also the similarity between the query and generated schema labels.
Deep neural networks have been widely used in computer vision.
Ranked #13 on Domain Adaptation on Office-31