Matches in SemOpenAlex for { <https://semopenalex.org/work/W4296998972> ?p ?o ?g. }
- W4296998972 endingPage "1976" @default.
- W4296998972 startingPage "1976" @default.
- W4296998972 abstract "In the present work, we propose a novel method utilizing only a decoder for generation of pseudo-examples, which has shown great success in image classification tasks. The proposed method is particularly constructive when the data are in a limited quantity used for semi-supervised learning (SSL) or few-shot learning (FSL). While most of the previous works have used an autoencoder to improve the classification performance for SSL, using a single autoencoder may generate confusing pseudo-examples that could degrade the classifier’s performance. On the other hand, various models that utilize encoder–decoder architecture for sample generation can significantly increase computational overhead. To address the issues mentioned above, we propose an efficient means of generating pseudo-examples by using only the generator (decoder) network separately for each class that has shown to be effective for both SSL and FSL. In our approach, the decoder is trained for each class sample using random noise, and multiple samples are generated using the trained decoder. Our generator-based approach outperforms previous state-of-the-art SSL and FSL approaches. In addition, we released the Urdu digits dataset consisting of 10,000 images, including 8000 training and 2000 test images collected through three different methods for purposes of diversity. Furthermore, we explored the effectiveness of our proposed method on the Urdu digits dataset by using both SSL and FSL, which demonstrated improvement of 3.04% and 1.50% in terms of average accuracy, respectively, illustrating the superiority of the proposed method compared to the current state-of-the-art models." @default.
- W4296998972 created "2022-09-25" @default.
- W4296998972 creator A5014772822 @default.
- W4296998972 creator A5033992808 @default.
- W4296998972 creator A5048556972 @default.
- W4296998972 creator A5068496291 @default.
- W4296998972 creator A5087514009 @default.
- W4296998972 date "2022-09-21" @default.
- W4296998972 modified "2023-10-09" @default.
- W4296998972 title "Introducing Urdu Digits Dataset with Demonstration of an Efficient and Robust Noisy Decoder-Based Pseudo Example Generator" @default.
- W4296998972 cites W1994616650 @default.
- W4296998972 cites W2009444210 @default.
- W4296998972 cites W2030443174 @default.
- W4296998972 cites W2040271314 @default.
- W4296998972 cites W2133552271 @default.
- W4296998972 cites W2328637565 @default.
- W4296998972 cites W2602159426 @default.
- W4296998972 cites W2618530766 @default.
- W4296998972 cites W2954996726 @default.
- W4296998972 cites W2964020599 @default.
- W4296998972 cites W2986378528 @default.
- W4296998972 cites W3045556127 @default.
- W4296998972 cites W3058737852 @default.
- W4296998972 cites W3105625590 @default.
- W4296998972 cites W3110028641 @default.
- W4296998972 cites W3134402217 @default.
- W4296998972 cites W3135604819 @default.
- W4296998972 cites W3175607202 @default.
- W4296998972 cites W3185526909 @default.
- W4296998972 cites W3202532686 @default.
- W4296998972 cites W3203548104 @default.
- W4296998972 cites W3211208177 @default.
- W4296998972 cites W4206720985 @default.
- W4296998972 cites W4210946681 @default.
- W4296998972 cites W4214740026 @default.
- W4296998972 cites W4220897028 @default.
- W4296998972 cites W4221059414 @default.
- W4296998972 cites W4221131057 @default.
- W4296998972 cites W4281555021 @default.
- W4296998972 cites W4293070540 @default.
- W4296998972 cites W4293550306 @default.
- W4296998972 cites W4297847013 @default.
- W4296998972 doi "https://doi.org/10.3390/sym14101976" @default.
- W4296998972 hasPublicationYear "2022" @default.
- W4296998972 type Work @default.
- W4296998972 citedByCount "33" @default.
- W4296998972 countsByYear W42969989722022 @default.
- W4296998972 countsByYear W42969989722023 @default.
- W4296998972 crossrefType "journal-article" @default.
- W4296998972 hasAuthorship W4296998972A5014772822 @default.
- W4296998972 hasAuthorship W4296998972A5033992808 @default.
- W4296998972 hasAuthorship W4296998972A5048556972 @default.
- W4296998972 hasAuthorship W4296998972A5068496291 @default.
- W4296998972 hasAuthorship W4296998972A5087514009 @default.
- W4296998972 hasBestOaLocation W42969989721 @default.
- W4296998972 hasConcept C101738243 @default.
- W4296998972 hasConcept C111919701 @default.
- W4296998972 hasConcept C118505674 @default.
- W4296998972 hasConcept C119857082 @default.
- W4296998972 hasConcept C121332964 @default.
- W4296998972 hasConcept C153180895 @default.
- W4296998972 hasConcept C154945302 @default.
- W4296998972 hasConcept C163258240 @default.
- W4296998972 hasConcept C2780992000 @default.
- W4296998972 hasConcept C2781213101 @default.
- W4296998972 hasConcept C28490314 @default.
- W4296998972 hasConcept C41008148 @default.
- W4296998972 hasConcept C50644808 @default.
- W4296998972 hasConcept C62520636 @default.
- W4296998972 hasConcept C95623464 @default.
- W4296998972 hasConceptScore W4296998972C101738243 @default.
- W4296998972 hasConceptScore W4296998972C111919701 @default.
- W4296998972 hasConceptScore W4296998972C118505674 @default.
- W4296998972 hasConceptScore W4296998972C119857082 @default.
- W4296998972 hasConceptScore W4296998972C121332964 @default.
- W4296998972 hasConceptScore W4296998972C153180895 @default.
- W4296998972 hasConceptScore W4296998972C154945302 @default.
- W4296998972 hasConceptScore W4296998972C163258240 @default.
- W4296998972 hasConceptScore W4296998972C2780992000 @default.
- W4296998972 hasConceptScore W4296998972C2781213101 @default.
- W4296998972 hasConceptScore W4296998972C28490314 @default.
- W4296998972 hasConceptScore W4296998972C41008148 @default.
- W4296998972 hasConceptScore W4296998972C50644808 @default.
- W4296998972 hasConceptScore W4296998972C62520636 @default.
- W4296998972 hasConceptScore W4296998972C95623464 @default.
- W4296998972 hasIssue "10" @default.
- W4296998972 hasLocation W42969989721 @default.
- W4296998972 hasOpenAccess W4296998972 @default.
- W4296998972 hasPrimaryLocation W42969989721 @default.
- W4296998972 hasRelatedWork W2292254049 @default.
- W4296998972 hasRelatedWork W2592385986 @default.
- W4296998972 hasRelatedWork W2610906757 @default.
- W4296998972 hasRelatedWork W2742860341 @default.
- W4296998972 hasRelatedWork W2897995864 @default.
- W4296998972 hasRelatedWork W2998168123 @default.
- W4296998972 hasRelatedWork W3032998101 @default.
- W4296998972 hasRelatedWork W3099179464 @default.
- W4296998972 hasRelatedWork W4281924768 @default.