97,075 research outputs found
CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation
Supervised neural approaches are hindered by their dependence on large,
meticulously annotated datasets, a requirement that is particularly cumbersome
for sequential tasks. The quality of annotations tends to deteriorate with the
transition from expert-based to crowd-sourced labelling. To address these
challenges, we present \textbf{CAMELL} (Confidence-based Acquisition Model for
Efficient self-supervised active Learning with Label validation), a pool-based
active learning framework tailored for sequential multi-output problems. CAMELL
possesses three core features: (1) it requires expert annotators to label only
a fraction of a chosen sequence, (2) it facilitates self-supervision for the
remainder of the sequence, and (3) it employs a label validation mechanism to
prevent erroneous labels from contaminating the dataset and harming model
performance. We evaluate CAMELL on sequential tasks, with a special emphasis on
dialogue belief tracking, a task plagued by the constraints of limited and
noisy datasets. Our experiments demonstrate that CAMELL outperforms the
baselines in terms of efficiency. Furthermore, the data corrections suggested
by our method contribute to an overall improvement in the quality of the
resulting datasets
Revisiting Class Imbalance for End-to-end Semi-Supervised Object Detection
Semi-supervised object detection (SSOD) has made significant progress with
the development of pseudo-label-based end-to-end methods. However, many of
these methods face challenges due to class imbalance, which hinders the
effectiveness of the pseudo-label generator. Furthermore, in the literature, it
has been observed that low-quality pseudo-labels severely limit the performance
of SSOD. In this paper, we examine the root causes of low-quality pseudo-labels
and present novel learning mechanisms to improve the label generation quality.
To cope with high false-negative and low precision rates, we introduce an
adaptive thresholding mechanism that helps the proposed network to filter out
optimal bounding boxes. We further introduce a Jitter-Bagging module to provide
accurate information on localization to help refine the bounding boxes.
Additionally, two new losses are introduced using the background and foreground
scores predicted by the teacher and student networks to improvise the
pseudo-label recall rate. Furthermore, our method applies strict supervision to
the teacher network by feeding strong & weak augmented data to generate robust
pseudo-labels so that it can detect small and complex objects. Finally, the
extensive experiments show that the proposed network outperforms
state-of-the-art methods on MS-COCO and Pascal VOC datasets and allows the
baseline network to achieve 100% supervised performance with much less (i.e.,
20%) labeled data.Comment: Accepted at the Efficient Deep Learning for Computer Vision Workshop,
CVPR 202
JointMatch: A Unified Approach for Diverse and Collaborative Pseudo-Labeling to Semi-Supervised Text Classification
Semi-supervised text classification (SSTC) has gained increasing attention
due to its ability to leverage unlabeled data. However, existing approaches
based on pseudo-labeling suffer from the issues of pseudo-label bias and error
accumulation. In this paper, we propose JointMatch, a holistic approach for
SSTC that addresses these challenges by unifying ideas from recent
semi-supervised learning and the task of learning with noise. JointMatch
adaptively adjusts classwise thresholds based on the learning status of
different classes to mitigate model bias towards current easy classes.
Additionally, JointMatch alleviates error accumulation by utilizing two
differently initialized networks to teach each other in a cross-labeling
manner. To maintain divergence between the two networks for mutual learning, we
introduce a strategy that weighs more disagreement data while also allowing the
utilization of high-quality agreement data for training. Experimental results
on benchmark datasets demonstrate the superior performance of JointMatch,
achieving a significant 5.13% improvement on average. Notably, JointMatch
delivers impressive results even in the extremely-scarce-label setting,
obtaining 86% accuracy on AG News with only 5 labels per class. We make our
code available at https://github.com/HenryPengZou/JointMatch.Comment: Accepted by EMNLP 2023 (Main
DiffuGen: Adaptable Approach for Generating Labeled Image Datasets using Stable Diffusion Models
Generating high-quality labeled image datasets is crucial for training
accurate and robust machine learning models in the field of computer vision.
However, the process of manually labeling real images is often time-consuming
and costly. To address these challenges associated with dataset generation, we
introduce "DiffuGen," a simple and adaptable approach that harnesses the power
of stable diffusion models to create labeled image datasets efficiently. By
leveraging stable diffusion models, our approach not only ensures the quality
of generated datasets but also provides a versatile solution for label
generation. In this paper, we present the methodology behind DiffuGen, which
combines the capabilities of diffusion models with two distinct labeling
techniques: unsupervised and supervised. Distinctively, DiffuGen employs prompt
templating for adaptable image generation and textual inversion to enhance
diffusion model capabilities
Learning from small and imbalanced dataset of images using generative adversarial neural networks.
The performance of deep learning models is unmatched by any other approach in supervised computer vision tasks such as image classification. However, training these models requires a lot of labeled data, which are not always available. Labelling a massive dataset is largely a manual and very demanding process. Thus, this problem has led to the development of techniques that bypass the need for labelling at scale. Despite this, existing techniques such as transfer learning, data augmentation and semi-supervised learning have not lived up to expectations. Some of these techniques do not account for other classification challenges, such as a class-imbalance problem. Thus, these techniques mostly underperform when compared with fully supervised approaches. In this thesis, we propose new methods to train a deep model on image classification with a limited number of labeled examples. This was achieved by extending state-of-the-art generative adversarial networks with multiple fake classes and network switchers. These new features enabled us to train a classifier using large unlabeled data, while generating class specific samples. The proposed model is label agnostic and is suitable for different classification scenarios, ranging from weakly supervised to fully supervised settings. This was used to address classification challenges with limited labeled data and a class-imbalance problem. Extensive experiments were carried out on different benchmark datasets. Firstly, the proposed approach was used to train a classification model and our findings indicated that the proposed approach achieved better classification accuracies, especially when the number of labeled samples is small. Secondly, the proposed approach was able to generate high-quality samples from class-imbalance datasets. The samples' quality is evident in improved classification performances when generated samples were used in neutralising class-imbalance. The results are thoroughly analyzed and, overall, our method showed superior performances over popular resampling technique and the AC-GAN model. Finally, we successfully applied the proposed approach as a new augmentation technique to two challenging real-world problems: face with attributes and legacy engineering drawings. The results obtained demonstrate that the proposed approach is effective even in extreme cases
Semi-Supervised Panoptic Narrative Grounding
Despite considerable progress, the advancement of Panoptic Narrative
Grounding (PNG) remains hindered by costly annotations. In this paper, we
introduce a novel Semi-Supervised Panoptic Narrative Grounding (SS-PNG)
learning scheme, capitalizing on a smaller set of labeled image-text pairs and
a larger set of unlabeled pairs to achieve competitive performance. Unlike
visual segmentation tasks, PNG involves one pixel belonging to multiple
open-ended nouns. As a result, existing multi-class based semi-supervised
segmentation frameworks cannot be directly applied to this task. To address
this challenge, we first develop a novel SS-PNG Network (SS-PNG-NW) tailored to
the SS-PNG setting. We thoroughly investigate strategies such as Burn-In and
data augmentation to determine the optimal generic configuration for the
SS-PNG-NW. Additionally, to tackle the issue of imbalanced pseudo-label
quality, we propose a Quality-Based Loss Adjustment (QLA) approach to adjust
the semi-supervised objective, resulting in an enhanced SS-PNG-NW+. Employing
our proposed QLA, we improve BCE Loss and Dice loss at pixel and mask levels,
respectively. We conduct extensive experiments on PNG datasets, with our
SS-PNG-NW+ demonstrating promising results comparable to fully-supervised
models across all data ratios. Remarkably, our SS-PNG-NW+ outperforms
fully-supervised models with only 30% and 50% supervision data, exceeding their
performance by 0.8% and 1.1% respectively. This highlights the effectiveness of
our proposed SS-PNG-NW+ in overcoming the challenges posed by limited
annotations and enhancing the applicability of PNG tasks. The source code is
available at https://github.com/nini0919/SSPNG.Comment: ACM MM 202
Semi-supervised Embedding in Attributed Networks with Outliers
In this paper, we propose a novel framework, called Semi-supervised Embedding
in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector
representation that systematically captures the topological proximity,
attribute affinity and label similarity of vertices in a partially labeled
attributed network (PLAN). Our method is designed to work in both transductive
and inductive settings while explicitly alleviating noise effects from
outliers. Experimental results on various datasets drawn from the web, text and
image domains demonstrate the advantages of SEANO over state-of-the-art methods
in semi-supervised classification under transductive as well as inductive
settings. We also show that a subset of parameters in SEANO is interpretable as
outlier score and can significantly outperform baseline methods when applied
for detecting network outliers. Finally, we present the use of SEANO in a
challenging real-world setting -- flood mapping of satellite images and show
that it is able to outperform modern remote sensing algorithms for this task.Comment: in Proceedings of SIAM International Conference on Data Mining
(SDM'18
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