96,169 research outputs found
Active Learning for Text Classification
Text classification approaches are used extensively to solve real-world challenges. The success or failure of text classification systems hangs on the datasets used to train them, without a good dataset it is impossible to build a quality system. This thesis examines the applicability of active learning in text classification for the rapid and economical creation of labelled training data. Four main contributions are made in this thesis. First, we present two novel selection strategies to choose the most informative examples for manually labelling. One is an approach using an advanced aggregated confidence measurement instead of the direct output of classifiers to measure the confidence of the prediction and choose the examples with least confidence for querying. The other is a simple but effective exploration guided active learning selection strategy which uses only the notions of density and diversity, based on similarity, in its selection strategy. Second, we propose new methods of using deterministic clustering algorithms to help bootstrap the active learning process. We first illustrate the problems of using non-deterministic clustering for selecting initial training sets, showing how non-deterministic clustering methods can result in inconsistent behaviour in the active learning process. We then compare various deterministic clustering techniques and commonly used non-deterministic ones, and show that deterministic clustering algorithms are as good as non-deterministic clustering algorithms at selecting initial training examples for the active learning process. More importantly, we show that the use of deterministic approaches stabilises the active learning process. Our third direction is in the area of visualising the active learning process. We demonstrate the use of an existing visualisation technique in understanding active learning selection strategies to show that a better understanding of selection strategies can be achieved with the help of visualisation techniques. Finally, to evaluate the practicality and usefulness of active learning as a general dataset labelling methodology, it is desirable that actively labelled dataset can be reused more widely instead of being only limited to some particular classifier. We compare the reusability of popular active learning methods for text classification and identify the best classifiers to use in active learning for text classification. This thesis is concerned using active learning methods to label large unlabelled textual datasets. Our domain of interest is text classification, but most of the methods proposed are quite general and so are applicable to other domains having large collections of data with high dimensionality
Efficient Image-Text Retrieval via Keyword-Guided Pre-Screening
Under the flourishing development in performance, current image-text
retrieval methods suffer from -related time complexity, which hinders their
application in practice. Targeting at efficiency improvement, this paper
presents a simple and effective keyword-guided pre-screening framework for the
image-text retrieval. Specifically, we convert the image and text data into the
keywords and perform the keyword matching across modalities to exclude a large
number of irrelevant gallery samples prior to the retrieval network. For the
keyword prediction, we transfer it into a multi-label classification problem
and propose a multi-task learning scheme by appending the multi-label
classifiers to the image-text retrieval network to achieve a lightweight and
high-performance keyword prediction. For the keyword matching, we introduce the
inverted index in the search engine and create a win-win situation on both time
and space complexities for the pre-screening. Extensive experiments on two
widely-used datasets, i.e., Flickr30K and MS-COCO, verify the effectiveness of
the proposed framework. The proposed framework equipped with only two embedding
layers achieves querying time complexity, while improving the retrieval
efficiency and keeping its performance, when applied prior to the common
image-text retrieval methods. Our code will be released.Comment: 11 pages, 7 figures, 6 table
ASPIRE: Language-Guided Augmentation for Robust Image Classification
Neural image classifiers can often learn to make predictions by overly
relying on non-predictive features that are spuriously correlated with the
class labels in the training data. This leads to poor performance in real-world
atypical scenarios where such features are absent. Supplementing the training
dataset with images without such spurious features can aid robust learning
against spurious correlations via better generalization. This paper presents
ASPIRE (Language-guided data Augmentation for SPurIous correlation REmoval), a
simple yet effective solution for expanding the training dataset with synthetic
images without spurious features. ASPIRE, guided by language, generates these
images without requiring any form of additional supervision or existing
examples. Precisely, we employ LLMs to first extract foreground and background
features from textual descriptions of an image, followed by advanced
language-guided image editing to discover the features that are spuriously
correlated with the class label. Finally, we personalize a text-to-image
generation model to generate diverse in-domain images without spurious
features. We demonstrate the effectiveness of ASPIRE on 4 datasets, including
the very challenging Hard ImageNet dataset, and 9 baselines and show that
ASPIRE improves the classification accuracy of prior methods by 1% - 38%. Code
soon at: https://github.com/Sreyan88/ASPIRE.Comment: Pre-print Under Revie
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