628 research outputs found
Boosting Semi-Supervised Learning by bridging high and low-confidence predictions
Pseudo-labeling is a crucial technique in semi-supervised learning (SSL),
where artificial labels are generated for unlabeled data by a trained model,
allowing for the simultaneous training of labeled and unlabeled data in a
supervised setting. However, several studies have identified three main issues
with pseudo-labeling-based approaches. Firstly, these methods heavily rely on
predictions from the trained model, which may not always be accurate, leading
to a confirmation bias problem. Secondly, the trained model may be overfitted
to easy-to-learn examples, ignoring hard-to-learn ones, resulting in the
\textit{"Matthew effect"} where the already strong become stronger and the weak
weaker. Thirdly, most of the low-confidence predictions of unlabeled data are
discarded due to the use of a high threshold, leading to an underutilization of
unlabeled data during training. To address these issues, we propose a new
method called ReFixMatch, which aims to utilize all of the unlabeled data
during training, thus improving the generalizability of the model and
performance on SSL benchmarks. Notably, ReFixMatch achieves 41.05\% top-1
accuracy with 100k labeled examples on ImageNet, outperforming the baseline
FixMatch and current state-of-the-art methods.Comment: Accepted to ICCVW2023 (Workshop on representation learning with very
limited images: the potential of self-, synthetic- and formula-supervision
Towards Data-centric Graph Machine Learning: Review and Outlook
Data-centric AI, with its primary focus on the collection, management, and
utilization of data to drive AI models and applications, has attracted
increasing attention in recent years. In this article, we conduct an in-depth
and comprehensive review, offering a forward-looking outlook on the current
efforts in data-centric AI pertaining to graph data-the fundamental data
structure for representing and capturing intricate dependencies among massive
and diverse real-life entities. We introduce a systematic framework,
Data-centric Graph Machine Learning (DC-GML), that encompasses all stages of
the graph data lifecycle, including graph data collection, exploration,
improvement, exploitation, and maintenance. A thorough taxonomy of each stage
is presented to answer three critical graph-centric questions: (1) how to
enhance graph data availability and quality; (2) how to learn from graph data
with limited-availability and low-quality; (3) how to build graph MLOps systems
from the graph data-centric view. Lastly, we pinpoint the future prospects of
the DC-GML domain, providing insights to navigate its advancements and
applications.Comment: 42 pages, 9 figure
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Debiasing, calibrating, and improving Semi-supervised Learning performance via simple Ensemble Projector
Recent studies on semi-supervised learning (SSL) have achieved great success.
Despite their promising performance, current state-of-the-art methods tend
toward increasingly complex designs at the cost of introducing more network
components and additional training procedures. In this paper, we propose a
simple method named Ensemble Projectors Aided for Semi-supervised Learning
(EPASS), which focuses mainly on improving the learned embeddings to boost the
performance of the existing contrastive joint-training semi-supervised learning
frameworks. Unlike standard methods, where the learned embeddings from one
projector are stored in memory banks to be used with contrastive learning,
EPASS stores the ensemble embeddings from multiple projectors in memory banks.
As a result, EPASS improves generalization, strengthens feature representation,
and boosts performance. For instance, EPASS improves strong baselines for
semi-supervised learning by 39.47\%/31.39\%/24.70\% top-1 error rate, while
using only 100k/1\%/10\% of labeled data for SimMatch, and achieves
40.24\%/32.64\%/25.90\% top-1 error rate for CoMatch on the ImageNet dataset.
These improvements are consistent across methods, network architectures, and
datasets, proving the general effectiveness of the proposed methods. Code is
available at https://github.com/beandkay/EPASS.Comment: Accepted to WACV 202
Conditional Consistency Regularization for Semi-supervised Multi-label Classification
In practical scenarios, a sample may have multiple labels that reveal its classes instead of a single label, which is widely known as multi-label classification (MLC). However, some practical situations may lack reliable labels due to the high cost, time-consuming and professional labelling process. Although Semi-supervised classification may become a potential solution, most of the outstanding existing methods are customized for the single-label situation and ignore multi-label situations. Consistency regularization has performed great success in Weakly/Semi-supervised Single-label classification (SS-SLC), but few efforts have been devoted to semi-supervised Multi-label classification (SS-MLC). A simple solution for introducing consistency regularization to SS-MLC is to regularize predictions of models to be consistent with different augmentation of the same image. Nonetheless, the solution lacks attention to label relations which are crucial components of Multi-label classification.
In the thesis, I go beyond the consistency regularization in SS-SLC and propose Conditional Consistency Regularization (CCR) that is designed for SS-MLC. To be specific, we make potential labels (grand-truth label for labeled samples, pseudo-label for unlabeled samples) conditioned on different label states (i.e., positive, negative, or unknown for each class). By regularizing the two predictions to be invariant, the model can learn label relations implicitly between two different label states, which can boost classification performance. The comprehensive experiments that are conducted on different datasets show that the proposed method can surpass state-of-art SS-MLC and MLC methods by a large gap
A Closer Look at Self-training for Zero-Label Semantic Segmentation
Being able to segment unseen classes not observed during training is an important technical challenge in deep learning, because of its potential to reduce the expensive annotation required for semantic segmentation. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. However, they are prone to overfitting on the seen classes because there is no training signal for them. In this paper, we study the challenging generalized zero-label semantic segmentation task where the model has to segment both seen and unseen classes at test time. We assume that pixels of unseen classes could be present in the training images but without being annotated. Our idea is to capture the latent information on unseen classes by supervising the model with self-produced pseudo-labels for unlabeled pixels. We propose a consistency regularizer to filter out noisy pseudo-labels by taking the intersections of the pseudo-labels generated from different augmentations of the same image. Our framework generates pseudo-labels and then retrain the model with human-annotated and pseudo-labelled data. This procedure is repeated for several iterations. As a result, our approach achieves the new state-of-the-art on PascalVOC12 and COCO-stuff datasets in the challenging generalized zero-label semantic segmentation setting, surpassing other existing methods addressing this task with more complex strategies. Code can be found at https://github. com/giuseppepastore10/STRICT
{CoSSL}: {C}o-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning
In this paper, we propose a novel co-learning framework (CoSSL) with decoupled representation learning and classifier learning for imbalanced SSL. To handle the data imbalance, we devise Tail-class Feature Enhancement (TFE) for classifier learning. Furthermore, the current evaluation protocol for imbalanced SSL focuses only on balanced test sets, which has limited practicality in real-world scenarios. Therefore, we further conduct a comprehensive evaluation under various shifted test distributions. In experiments, we show that our approach outperforms other methods over a large range of shifted distributions, achieving state-of-the-art performance on benchmark datasets ranging from CIFAR-10, CIFAR-100, ImageNet, to Food-101. Our code will be made publicly available
Semi-supervised learning and fairness-aware learning under class imbalance
With the advent of Web 2.0 and the rapid technological advances, there is a plethora of data in every field; however, more data does not necessarily imply more information, rather the quality of data (veracity aspect) plays a key role. Data quality is a major issue, since machine learning algorithms are solely based on historical data to derive novel hypotheses. Data may contain noise, outliers, missing values and/or class labels, and skewed data distributions. The latter case, the so-called class-imbalance problem, is quite old and still affects dramatically machine learning algorithms. Class-imbalance causes classification models to learn effectively one particular class (majority) while ignoring other classes (minority). In extend to this issue, machine learning models that are applied in domains of high societal impact have become biased towards groups of people or individuals who are not well represented within the data. Direct and indirect discriminatory behavior is prohibited by international laws; thus, there is an urgency of mitigating discriminatory outcomes from machine learning algorithms.
In this thesis, we address the aforementioned issues and propose methods that tackle class imbalance, and mitigate discriminatory outcomes in machine learning algorithms. As part of this thesis, we make the following contributions:
• Tackling class-imbalance in semi-supervised learning – The class-imbalance problem is very often encountered in classification. There is a variety of methods that tackle this problem; however, there is a lack of methods that deal with class-imbalance in the semi-supervised learning. We address this problem by employing data augmentation in semi-supervised learning process in order to equalize class distributions. We show that semi-supervised learning coupled with data augmentation methods can overcome class-imbalance propagation and significantly outperform the standard semi-supervised annotation process.
• Mitigating unfairness in supervised models – Fairness in supervised learning has received a lot of attention over the last years. A growing body of pre-, in- and postprocessing approaches has been proposed to mitigate algorithmic bias; however, these methods consider error rate as the performance measure of the machine learning algorithm, which causes high error rates on the under-represented class. To deal with this problem, we propose approaches that operate in pre-, in- and post-processing layers while accounting for all classes. Our proposed methods outperform state-of-the-art methods in terms of performance while being able to mitigate unfair outcomes
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