Chen et al., 2021 - Google Patents
Supercharging imbalanced data learning with energy-based contrastive representation transferChen et al., 2021
View PDF- Document ID
- 10778774199177050175
- Author
- Chen J
- Xiu Z
- Goldstein B
- Henao R
- Carin L
- Tao C
- Publication year
- Publication venue
- Advances in neural information processing systems
External Links
Snippet
Dealing with severe class imbalance poses a major challenge for many real-world applications, especially when the accurate classification and generalization of minority classes are of primary interest. In computer vision and NLP, learning from datasets with long …
- 230000003416 augmentation 0 abstract description 50
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6296—Graphical models, e.g. Bayesian networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Dai et al. | Adversarial training methods for network embedding | |
Cui et al. | A novel multi-module integrated intrusion detection system for high-dimensional imbalanced data | |
Rosenfeld et al. | Domain-adjusted regression or: Erm may already learn features sufficient for out-of-distribution generalization | |
Chen et al. | Supercharging imbalanced data learning with energy-based contrastive representation transfer | |
CN113536383B (en) | Method and device for training graph neural network based on privacy protection | |
Hayashi et al. | Less complexity one-class classification approach using construction error of convolutional image transformation network | |
Dong et al. | Recognition of imbalanced underwater acoustic datasets with exponentially weighted cross-entropy loss | |
Zhao et al. | Multiple source domain adaptation with adversarial training of neural networks | |
Zhang et al. | Fine-tuning graph neural networks via graph topology induced optimal transport | |
Kong et al. | Robust meta-learning for mixed linear regression with small batches | |
Wang et al. | Clusterscl: Cluster-aware supervised contrastive learning on graphs | |
Sulaiman et al. | Credit Card Fraud Detection Using Improved Deep Learning Models. | |
Wang et al. | Posterior collapse of a linear latent variable model | |
Li et al. | Max-margin deep generative models | |
Lee et al. | Removing undesirable feature contributions using out-of-distribution data | |
Zheng et al. | Energy-efficient resource allocation in generative ai-aided secure semantic mobile networks | |
Luo et al. | A novel oversampling method based on SeqGAN for imbalanced text classification | |
EP4288912B1 (en) | Method and system for training a neural network for improving adversarial robustness | |
Chen et al. | Mutual variational inference: An indirect variational inference approach for unsupervised domain adaptation | |
Larabi-Marie-Sainte et al. | Improving spam email detection using deep recurrent neural network | |
Sarhan et al. | Fake Accounts Detection in Online So-cial Networks Using Hybrid Machine Learning Models | |
US7502495B2 (en) | Method and system for incrementally learning an adaptive subspace by optimizing the maximum margin criterion | |
Barkam et al. | Hyperdimensional computing for resilient edge learning | |
CN118349633A (en) | Multi-constraint guided Chinese rumor countermeasure sample generation method, system and storage medium | |
Chen et al. | Preserving domain private representation via mutual information maximization |