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Is Discriminator a Good Feature Extractor? [article]

Xin Mao, Zhaoyu Su, Pin Siang Tan, Jun Kang Chow, Yu-Hsing Wang
2020 arXiv   pre-print
This makes the features more robust and helps answer the question as to why the discriminator can succeed as a feature extractor in related research.  ...  without noise is indeed allowed and occupies a large proportion of the feature space.  ...  This helps check the possibilities a and b in the Introduction and helps us understand why a discriminator based feature extractor can achieve good performance in downstream tasks.  ... 
arXiv:1912.00789v2 fatcat:zhn4igmyzncldfzlka5xvtr4qq


Bifan Wei, Jun Liu, Jian Ma, Qinghua Zheng, Wei Zhang, Boqin Feng
2013 Proceedings of the 22nd International Conference on World Wide Web - WWW '13 Companion  
by using a modified topical crawler. 2) Then it exploits a classification model to extract hyponym relations with the use of motif-based features. 3) Finally, it constructs a faceted taxonomy by applying  ...  DFT-Extractor also provides a graphical user interface to visualize the learned hyponym relations and the tree structure of taxonomies.  ...  Good performance can be achieved without further combining text features.  ... 
doi:10.1145/2487788.2487922 dblp:conf/www/WeiLMZZF13 fatcat:rgmfet4qtrgvfms7ay66t4idii

Principal Component Analysis-Linear Discriminant Analysis Feature Extractor for Pattern Recognition [article]

Aamir Khan, Hasan Farooq
2012 arXiv   pre-print
This paper is aimed at investigating a biometric identity system using Principal Component Analysis and Lindear Discriminant Analysis with K-Nearest Neighbor and implementing such system in real-time using  ...  Results show good pattern by multimodal biometric system proposed in this paper.  ...  Accordingly, the features produced by PCA are not necessarily good for discriminant among classes.  ... 
arXiv:1204.1177v1 fatcat:ky6frblv4bgtvntmrgumyehj2u

Complex Network based Supervised Keyword Extractor

Swagata Duari, Vasudha Bhatnagar
2019 Expert systems with applications  
The training set is created from the feature set by assigning a label to each candidate keyword depending on whether the candidate is listed as a gold-standard keyword or not.  ...  Since the number of keywords in a document is much less than non-keywords, the curated training set is naturally imbalanced.  ...  Since eliciting good quality features is crucial for performance of the trained model, feature construction is recognized as the focal task in creation of training set for supervised KE approaches.  ... 
doi:10.1016/j.eswa.2019.112876 fatcat:iov4z427ird4nd62ujnsreyepm

Random Forest as a Tumour Genetic Marker Extractor [article]

Raquel Pérez-Arnal, Dario Garcia-Gasulla, David Torrents, Ferran Parés, Ulises Cortés, Jesús Labarta, Eduard Ayguadé
2019 arXiv   pre-print
Using a Random Forest classifier, we evaluate the relevance of several features (some directly available in the original data, some engineered by us) related to chromosome rearrangements.  ...  Finding tumour genetic markers is essential to biomedicine due to their relevance for cancer detection and therapy development.  ...  Cortés is a member of the Sistema Nacional de Investigadores (Level III) (SNI-III). México. We would like to thank MD. Adrián Puche Gallego and PhD. Davide Cirillo for useful discussions and guidance.  ... 
arXiv:1911.11471v1 fatcat:ebiztkufx5hvnpedj4yztibx4q

Inadequately Pre-trained Models are Better Feature Extractors [article]

Andong Deng, Xingjian Li, Zhibing Li, Di Hu, Chengzhong Xu, Dejing Dou
2022 arXiv   pre-print
However, in this paper, we found that during the same pre-training process, models at middle epochs, which is inadequately pre-trained, can outperform fully trained models when used as feature extractors  ...  This reveals that there is not a solid positive correlation between top-1 accuracy on ImageNet and the transferring result on target data.  ...  Based on this observation, we can operate a more sophisticated checkpoint selection process when we need a good feature extractor trained from source data [5] .  ... 
arXiv:2203.04668v1 fatcat:5g6f2syvqrgblalwfw2jmln6ku

Green coffee beans feature extractor using image processing

Edwin R. Arboleda, Arnel C. Fajardo, Ruji P. Medina
2020 TELKOMNIKA (Telecommunication Computing Electronics and Control)  
Since the two problems are brought about by the complex mathematical operations being used by the algorithms, these were replaced by a discriminant.  ...  The developed discriminant, equivalent to the product of total difference and intensity divided by the normalization values, is based on the "pixel pair formation" that produces optimal peak signal to  ...  It only needs a camera, a lighting mechanism, a computer or a microcontroller and a good algorithm for feature extraction [28, 29] .  ... 
doi:10.12928/telkomnika.v18i4.13968 fatcat:lyod55i57zeldel7j5hk23wc2a

Feature Extractors for Describing Vehicle Routing Problem Instances

Jussi Rasku, Tommi Kärkkäinen, Nysret Musliu, Marc Herbstritt
2016 Student Conference on Operational Research  
As a necessary intermediate step towards this goal, we propose a set of feature extractors for vehicle routing problems.  ...  Unfortunately, finding a good set of values for these parameters can be a tedious task that requires extensive experimentation and experience.  ...  Therefore, a good set of feature extractors is a critical prerequisite for employing these learning meta-optimization techniques.  ... 
doi:10.4230/ dblp:conf/scor/RaskuKM16 fatcat:muv3u4qdd5fi5lltna3ugixv4i

PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs [article]

Vidhya Kamakshi, Uday Gupta, Narayanan C Krishnan
2021 arXiv   pre-print
To the best of our knowledge, this is the first work that extracts class-specific discriminative concepts in a posthoc manner automatically.  ...  There is a growing interest in explaining the working of these deep models to improve their trustworthiness.  ...  a good separation [33] .  ... 
arXiv:2108.13828v1 fatcat:owt3ys2gtzhphbvndtdxqy5nku

Discriminatively Re-trained i-vector Extractor for Speaker Recognition [article]

Ondrej Novotny, Oldrich Plchot, Ondrej Glembek, Lukas Burget, Pavel Matejka
2018 arXiv   pre-print
In this work we revisit discriminative training of the i-vector extractor component in the standard speaker verification (SV) system.  ...  We show that after generative initialization of the i-vector extractor, we can further refine it with discriminative training and obtain i-vectors that lead to better performance on various benchmarks  ...  Discriminatively Trained i-vector Extractor Traditionally, matrix T is trained in a generative fashion using the EM algorithm.  ... 
arXiv:1810.13183v1 fatcat:gzu5lsm5ybdmnffudpf2ygldai

Feature Learning for Chord Recognition: The Deep Chroma Extractor [article]

Filip Korzeniowski, Gerhard Widmer
2016 arXiv   pre-print
This leads us to propose a learned chroma feature extractor based on artificial neural networks.  ...  It is trained to compute chroma features that encode harmonic information important for chord recognition, while being robust to irrelevant interferences.  ...  To further assess how useful the extracted features are for chord recognition, we shall investigate how well they interact with post-filtering methods; since the feature extractor is trained discriminatively  ... 
arXiv:1612.05065v1 fatcat:5yy4m5m5wfhojnrlyhsbiah34u

How to train your speaker embeddings extractor

Mitchell Mclaren, Diego Castán, Mahesh Kumar Nandwana, Luciana Ferrer, Emre Yilmaz
2018 Odyssey 2018 The Speaker and Language Recognition Workshop  
In this study, we aim to explore some of the fundamental requirements for building a good speaker embeddings extractor.  ...  We analyze the impact of voice activity detection, types of degradation, the amount of degraded data, and number of speakers required for a good network.  ...  For the research community, the publiclyavailable Kaldi software provides a recipe for training a good embeddings extractor [7] .  ... 
doi:10.21437/odyssey.2018-46 dblp:conf/odyssey/McLarenCNFY18 fatcat:xx2sk5xpxbduvetibcaafenuti

Feature Learning For Chord Recognition: The Deep Chroma Extractor

Filip Korzeniowski, Gerhard Widmer
2016 Zenodo  
To further assess how useful the extracted features are for chord recognition, we shall investigate how well they interact with post-filtering methods; since the feature extractor is trained discriminatively  ...  Since training logistic regression is a convex problem, the result is at least as good as if we used a running mean. 3 4 Chord annotations available at  ... 
doi:10.5281/zenodo.1416314 fatcat:fs376larhnabznwcbwvl3bj2ya

MultiResolution Attention Extractor for Small Object Detection [article]

Fan Zhang, Licheng Jiao, Lingling Li, Fang Liu, Xu Liu
2020 arXiv   pre-print
Then we present the second method, an attention-based feature interaction method, called a MultiResolution Attention Extractor (MRAE), showing significant improvement as a generic feature extractor in  ...  After each building block in the vanilla feature extractor, we append a small network to generate attention weights followed by a weighted-sum operation to get the final attention maps.  ...  Generative adversarial network is good at generating fake distribution similar to the input distribution. It utilizes a generator to deceive the discriminator to achieve a Nash equilibrium.  ... 
arXiv:2006.05941v1 fatcat:qt57o2i6dncbnlhi7bbsab37uu

Incremental update of feature extractor for camera identification

Ruizhe Li, Chang-Tsun Li, Yu Guan
2015 2015 IEEE International Conference on Image Processing (ICIP)  
A feature extractor based on this concept was applied to extract a small number of components which contain most of the discriminative information of sensor fingerprint [14] .  ...  By excluding these redundant features, we can extract a set of features which contain most of the discriminative information of SPN signal.  ... 
doi:10.1109/icip.2015.7350813 dblp:conf/icip/LiLG15 fatcat:it5xm4wuc5autfxtsamkidc3fa
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