Easy balanced mixing for long-tailed data
Weblong-tailed data for image tasks [4,10,22,29,40,42,47, 49,53]. Two popular directions to balance class distribu-tions are re-sampling and re-weighting. Re-sampling [8, 11,16,22,53] methods up-sample tail classes and down-sample head classes to acquire a balanced data distribution from the original data. On the other hand, re-weighting WebSep 21, 2024 · In this paper, we propose Balanced-MixUp, a new imbalanced-robust training method that mixes up imbalanced (instance-based) and balanced (class-based) …
Easy balanced mixing for long-tailed data
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WebOct 7, 2024 · In this section, we first analyze the underlying issues of long-tailed data that affect model performance (Sect. 3.1), and then explore deeper into the feature space of DNNs and illustrate a novel way to alleviate the problem (Sect. 3.2). 3.1 Two Reasons of Model Performance Drop. Long-tailed data hurt the performance of learning-based … WebApr 1, 2024 · Request PDF Easy balanced mixing for long-tailed data In long-tailed datasets, head classes occupy most of the data, while tail classes have very few …
WebJul 19, 2024 · In long-tailed data, the greatest challenge is the lack of tail information, which creates difficulties in recognizing unseen tail samples. To this end, this work proposes an easy balanced mixing framework (EZBM) that extends the decision region for tail … WebEasy balanced mixing for long-tailed data. Z Zhu, H Xing, Y Xu. Knowledge-Based Systems 248, 108816, 2024. 1: 2024: Efficient matrixized classification learning with …
WebModern real-world large-scale datasets often have long-tailed label distributions [51, 28, 34, 12, 15, 50, 40]. On these datasets, deep neural networks have been found to perform poorly on less represented classes [17, 51, 5]. This is particularly detrimental if the testing criterion places more emphasis on minority classes. WebMar 22, 2024 · To address the challenges of long-tailed classification, researchers have proposed several approaches to reduce model bias, most of which assume that classes with few samples are weak classes. However, recent studies have shown that tail classes are not always hard to learn, and model bias has been observed on sample-balanced …
Webfully create more balanced datasets (e.g., ImageNet [12], MSCOCO [38], and Kinetics-400 [31]) with human labors or develop more robust algorithms to handle data imbal-ance. However, since the former is notoriously laborious and expensive, many researchers have been devoted to the latter. Formally, long-tailed recognition (LTR) is a research 1
Webet al.,2024). From our extensive study across three long-tail datasets, ImageNet-LT, Places-LT and iNaturalist, we make the following intriguing observations: •We find that decoupling representation learning and classification has surprising results that challenge common beliefs for long-tailed recognition: instance-balanced sampling learns ceph dashboard restartWebNov 1, 2024 · Such invariance can reduce “hard” noises to “easy” ones. Specifically, we sample three data distribution: long-tailed, balanced, and reversed long-tailed, as three context environments, and then apply Invariant Risk Minimization (IRM) to learn a long-tailed classifier as the noise identifier invariant to these environments. Note that ... ceph dispatchWebMar 22, 2024 · Finally, to approximately maximize the mutual information between the two views, we propose Siamese Balanced Softmax and joint it with the contrastive loss for one-stage training. Extensive experiments demonstrate that ResCom outperforms the previous methods by large margins on multiple long-tailed recognition benchmarks. ceph df usedWebespecially in balanced data scenarios. Though, real-world data is usually severely imbalanced, following a long-tailed distribution [71,55,34,35], i.e., very few fre-quent classes take up the majority of data (head) while most classes are in-frequent (tail). The highly biased data skews classifier learning and leads to performance drop on tail ... buy pineapple power sharesWebThe imbalanced distribution of long-tailed data leads classifiers to overfit the data in head classes and mismatch with the training and testing distributions, especially for tail … buy pinch pleat curtain tapeWebAug 25, 2016 · The Two Types of Self-Service Data Preparation Tools. Data preparation and blending features are found in two types of self-service tools: Visual analytics … buy pin connectorsWebLong-tailed classification. For the long-tailed classifi-cation task, there is a rich body of widely used meth-ods including data re-sampling [3] and re-weighting [2,7]. Recent works [19,48] reveal the effectiveness of using different sampling schemes in decoupled training stages. Instance-balanced sampling is found useful for the first fea ... ceph drph competencies