Results 31 to 40 of about 16,769 (297)
Data Consistency for Weakly Supervised Learning
CoRR, 2022 In many applications, training machine learning models involves using large amounts of human-annotated data. Obtaining precise labels for the data is expensive. Instead, training with weak supervision provides a low-cost alternative. We propose a novel weak supervision algorithm that processes noisy labels, i.e., weak signals, while also considering ...
Chidubem Arachie, Bert Huang
openaire +2 more sources
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation [PDF]
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 2021 Paraphrase generation is a longstanding NLP task that has diverse applications for downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to address this issue, they may fail to generate meaningful paraphrases due to the lack of ...
Kaize Ding +6 more
openaire +2 more sources
Lesion region segmentation via weakly supervised learning
Quantitative Biology, 2022 Background Image‐based automatic diagnosis of field diseases can help increase crop yields and is of great importance. However, crop lesion regions tend to be scattered and of varying sizes, this along with substantial intra‐class variation and small ...
Ran Yi +5 more
doaj +1 more source
A review of intelligent diagnosis methods of imaging gland cancer based on machine learning
Virtual Reality & Intelligent Hardware, 2023 Background: Gland cancer is a high-incidence disease endangering human health, and its early detection and treatment need efficient, accurate and objective intelligent diagnosis methods.
Han Jiang +5 more
doaj +1 more source
Weakly Supervised Dictionary Learning
IEEE Transactions on Signal Processing, 2018 We present a probabilistic modeling and inference framework for discriminative analysis dictionary learning under a weak supervision setting. Dictionary learning approaches have been widely used for tasks such as low-level signal denoising and restoration as well as high-level classification tasks, which can be applied to audio and image analysis ...
Zeyu You +3 more
openaire +2 more sources
Weakly Supervised Learning Approach for Implicit Aspect Extraction
Information, 2023 Aspect-based sentiment analysis (ABSA) is a process to extract an aspect of a product from a customer review and identify its polarity. Most previous studies of ABSA focused on explicit aspects, but implicit aspects have not yet been the subject of much ...
Aye Aye Mar +2 more
doaj +1 more source
Deep learning models (Weakly Supervised Baseline Precision and Recall). [PDF]
, 2023 Deep learning models (Weakly Supervised Baseline Precision and Recall).
Abdul Ghafoor (849371) +5 more
core +1 more source
Weakly Supervised Deep Learning for Tooth-Marked Tongue Recognition
Frontiers in Physiology, 2022 The recognition of tooth-marked tongues has important value for clinical diagnosis of traditional Chinese medicine. Tooth-marked tongue is often related to spleen deficiency, cold dampness, sputum, effusion, and blood stasis.
Jianguo Zhou +8 more
doaj +1 more source
Constrained Labeling for Weakly Supervised Learning
CoRR, 2020 Accepted at UAI ...
Chidubem Arachie, Bert Huang
openaire +3 more sources
Weakly Supervised Learning of Affordances
CoRR, 2016 Localizing functional regions of objects or affordances is an important aspect of scene understanding. In this work, we cast the problem of affordance segmentation as that of semantic image segmentation. In order to explore various levels of supervision, we introduce a pixel-annotated affordance dataset of 3090 images containing 9916 object instances ...
Abhilash Srikantha, Juergen Gall
openaire +2 more sources