The feature generator of hard negative samples for fine-grained image recognition
Sign Up to like & getrecommendations! Published in 2021 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.10.032
Abstract: Abstract The key to solving the fine-grained image recognition is exploring more discriminative features for capturing tiny hints. In particular, the triplet objective function fits well with the fine-grained image recognition task because they capture… read more here.
Keywords: fine grained; grained image; image recognition; negative samples ... See more keywords
Reliable assessment approach of landslide susceptibility in broad areas based on optimal slope units and negative samples involving priori knowledge
Sign Up to like & getrecommendations! Published in 2022 at "International Journal of Digital Earth"
DOI: 10.1080/17538947.2022.2159549
Abstract: ABSTRACT Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid… read more here.
Keywords: negative samples; reliable assessment; knowledge; susceptibility ... See more keywords
Welding Surface Inspection of Armatures via CNN and Image Comparison
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Sensors Journal"
DOI: 10.1109/jsen.2021.3079334
Abstract: This paper proposes a new method for detecting defects on the welding surface of armature based on image comparison and Convolutional Neural Network (CNN). General classification methods based on CNN need strict boundaries on the… read more here.
Keywords: welding surface; negative samples; cnn image; image ... See more keywords
FALSE: False Negative Samples Aware Contrastive Learning for Semantic Segmentation of High-Resolution Remote Sensing Image
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Geoscience and Remote Sensing Letters"
DOI: 10.1109/lgrs.2022.3222836
Abstract: Self-supervised contrastive learning (SSCL) is a potential learning paradigm for learning remote sensing image (RSI)-invariant features through the label-free method. The existing SSCL of RSI is built based on constructing positive and negative sample pairs.… read more here.
Keywords: negative samples; contrastive learning; fns; semantic segmentation ... See more keywords
Prediction of Drug–Target Interactions Based on Network Representation Learning and Ensemble Learning
Sign Up to like & getrecommendations! Published in 2021 at "IEEE/ACM Transactions on Computational Biology and Bioinformatics"
DOI: 10.1109/tcbb.2020.2989765
Abstract: Identifying interactions between drugs and target proteins is a critical step in the drug development process, as it helps identify new targets for drugs and accelerate drug development. The number of known drug–protein interactions (positive… read more here.
Keywords: negative samples; drug; drug protein; prediction ... See more keywords
Prediction of Drug Side Effects with a Refined Negative Sample Selection Strategy
Sign Up to like & getrecommendations! Published in 2020 at "Computational and Mathematical Methods in Medicine"
DOI: 10.1155/2020/1573543
Abstract: Drugs are an important way to treat various diseases. However, they inevitably produce side effects, bringing great risks to human bodies and pharmaceutical companies. How to predict the side effects of drugs has become one… read more here.
Keywords: drug; selection strategy; strategy; negative samples ... See more keywords
How to balance the bioinformatics data: pseudo-negative sampling
Sign Up to like & getrecommendations! Published in 2019 at "BMC Bioinformatics"
DOI: 10.1186/s12859-019-3269-4
Abstract: Imbalanced datasets are commonly encountered in bioinformatics classification problems, that is, the number of negative samples is much larger than that of positive samples. Particularly, the data imbalance phenomena will make us underestimate the performance… read more here.
Keywords: positive samples; pseudo negative; negative sampling; negative samples ... See more keywords
On TCR binding predictors failing to generalize to unseen peptides
Sign Up to like & getrecommendations! Published in 2022 at "Frontiers in Immunology"
DOI: 10.3389/fimmu.2022.1014256
Abstract: Several recent studies investigate TCR-peptide/-pMHC binding prediction using machine learning or deep learning approaches. Many of these methods achieve impressive results on test sets, which include peptide sequences that are also included in the training… read more here.
Keywords: negative samples; unseen peptides; tcr binding; generalize unseen ... See more keywords
Negative Samples for Improving Object Detection—A Case Study in AI-Assisted Colonoscopy for Polyp Detection
Sign Up to like & getrecommendations! Published in 2023 at "Diagnostics"
DOI: 10.3390/diagnostics13050966
Abstract: Deep learning object-detection models are being successfully applied to develop computer-aided diagnosis systems for aiding polyp detection during colonoscopies. Here, we evidence the need to include negative samples for both (i) reducing false positives during… read more here.
Keywords: negative samples; polyp detection; samples improving; detection ... See more keywords
Detecting Errors with Zero-Shot Learning
Sign Up to like & getrecommendations! Published in 2022 at "Entropy"
DOI: 10.3390/e24070936
Abstract: Error detection is a critical step in data cleaning. Most traditional error detection methods are based on rules and external information with high cost, especially when dealing with large-scaled data. Recently, with the advances of… read more here.
Keywords: negative samples; error detection; error; deep learning ... See more keywords