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Question 3 of 8 Damiens model can learn | StudyX Zero-shot learning is a type of machine learning where a model can recognize or classify data it has never seen before during training This is achieved by associating the unseen data with known information through descriptions or attributes
Damiens model can learn from information that is new to it without . . . Damien's model can learn from information that is new to it without needing to be retrained Which learning capability does his model have? (A) zero-shot learning (B) multi-instance learning (C) reinforcement learning (D) transfer learning Add your answer Asked by ProfS5751 • 07 09 2025 Read More
Smarter Deep Learning Model Adapts Like Humans, Learns Without Full . . . A team of Katz School researchers has solved one of the biggest headaches in modern machine learning—how to make AI models that can adapt to new information without needing to start over from scratch—by developing a deep learning system that updates itself using new information about the world without retraining, and does so in a way that
machine learning - Should a model be re-trained if new observations are . . . When new observations are available, there are three ways to retrain your model: Online: each time a new observation is available, you use this single data point to further train your model (e g load your current model and further train it by doing backpropagation with that single observation)
Extending Deep Learning to New Classes without Retraining NNs) suffer from the assumption of a closed-world model That is, once a model is learned, a new class cannot usually be dded without changes in the architecture and retraining Herein, we put forth a way to extend a number of deep learning algorithms while keeping their features in a locked state
What Is Few-Shot Learning? - Coursera Few-shot learning is a type of “meta-learning,” which teaches models to adapt to new tasks by “learning how to learn ” The goal is for machine learning models to generalize from just a few examples, similar to how humans can apply knowledge to a new situation without needing extensive practice
To retrain, or not to retrain? Lets get analytical about ML model updates Defining the retraining strategy in advance Check #1 How much data is needed? Check #2 How quickly will the quality drop? Check #3 When do you get the new data? Check #4 How often should you retrain? Check #5 Should you drop the old data? Part 2 Monitoring the model performance Part 3 Retraining vs Updates
Reshaping the Model’s Memory without the Need for Retraining This new study shows how it is not enough to focus on the key terms of a concept (for example, the main characters in Harry Potter) but also on the concept itself (the plot for example) The authors show how the model loses familiarity with Harry Potter and at the same time maintains its performance in reasoning benchmarks