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A systematic literature review of novelty detection in data streams: challenges and opportunities
Novelty detection in data streams is the task of detecting concepts that were not known prior,
in streams of data. Many machine learning algorithms have been proposed to detect these …
in streams of data. Many machine learning algorithms have been proposed to detect these …
Overcoming catastrophic forgetting during domain adaptation of seq2seq language generation
Latent coreset sampling based data-free continual learning
Catastrophic forgetting poses a major challenge in continual learning where the old
knowledge is forgotten when the model is updated on new tasks. Existing solutions tend to …
knowledge is forgotten when the model is updated on new tasks. Existing solutions tend to …
Lpc: A logits and parameter calibration framework for continual learning
When we execute the typical fine-tuning paradigm on continuously sequential tasks, the
model will suffer from the catastrophic forgetting problem (ie, the model tends to adjust old …
model will suffer from the catastrophic forgetting problem (ie, the model tends to adjust old …
A theoretical analysis of out-of-distribution detection in multi-label classification
The ability to detect out-of-distribution (OOD) inputs is essential for safely deploying machine
learning models in an open world. Most existing research on OOD detection, and more …
learning models in an open world. Most existing research on OOD detection, and more …
Dual contrastive learning framework for incremental text classification
Incremental learning plays a pivotal role in the context of online knowledge discovery, as it
encourages large models (LM) to learn and refresh knowledge continuously. Many …
encourages large models (LM) to learn and refresh knowledge continuously. Many …
Novelty detection for multi-label stream classification under extreme verification latency
Abstract Multi-Label Stream Classification (MLSC) is the classification streaming examples
into multiple classes simultaneously. Since new classes may emerge during the streaming …
into multiple classes simultaneously. Since new classes may emerge during the streaming …
A deep decomposable model for disentangling syntax and semantics in sentence representation
Recently, disentanglement based on a generative adversarial network or a variational
autoencoder has significantly advanced the performance of diverse applications in CV and …
autoencoder has significantly advanced the performance of diverse applications in CV and …