Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment

Guo, Lan-Zhe; Shao, Jie-Jing; Zhang, Qi; Kuang, Feng; Li, Gao-Le; Liu, Zhang-Xun; Wu, Guobin; Ma, Nan; Qun, LI; Li, Yufeng

doi:10.1145/3447548.3467305

Cited by 7 publications

(2 citation statements)

References 14 publications

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“…In this expression, N is the number of feature vectors in a mini-batch of the feature map. 32 is the dimensionality-reduced training set consisting of the feature vectors z (i) ∈ R 64 . For dimensionality reduction, we use principal component analysis (PCA) [58], which computes the principal components and use only the first few principal components corresponding to the largest eigenvalues for manageable computational complexity.…”

Section: B Two Objective Functionsmentioning

confidence: 99%

“…A common approach to tackle the class imbalance problem is to allocate class importance to mitigate the imbalance based on the class distribution. This includes rebalancing the class weights [62], [63], [64] and regulating the learning frequency by sampling [22], [53], [65]. Table I shows that the echosounder data are severely class-imbalanced to the given classes, where more than 99% of the backscattering intensities belong to the background (BG) class consisting of the water and seabed features.…”

Section: Advance On the Semisupervised Image Classification Formentioning

confidence: 99%

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Deep Semisupervised Semantic Segmentation in Multifrequency Echosounder Data

Choi

Kampffmeyer

Handegard

et al. 2023

IEEE J. Oceanic Eng.

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Multifrequency echosounder data can provide a broad understanding of the underwater environment in a noninvasive manner. The analysis of echosounder data is, hence, a topic of great importance for the marine ecosystem. Semantic segmentation, a deep learning-based analysis method predicting the class attribute of each acoustic intensity, has recently been in the spotlight of the fisheries and aquatic industry since its result can be used to estimate the abundance of marine organisms. However, a fundamental problem with current methods is the massive reliance on the availability of large amounts of annotated training data, which can only be acquired through expensive handcrafted annotation processes, making such approaches unrealistic in practice. As a solution to this challenge, we propose a novel approach, where we leverage a small amount of annotated data (supervised deep learning) and a large amount of readily available unannotated data (unsupervised learning), yielding a new data-efficient and accurate semisupervised semantic segmentation method, all embodied into a single end-to-end trainable convolutional neural network architecture. Our method is evaluated on representative data from a sandeel survey in the North Sea conducted by the Norwegian Institute of Marine Research. The rigorous experiments validate that our method achieves comparable results utilizing only 40% of the annotated data on which the supervised method is trained, by leveraging unannotated data.

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Section: B Two Objective Functionsmentioning

confidence: 99%