Few-shot Learning
Few-shot Learning - Related Videos
07:47
Use of Sacrificial Nanoparticles to Remove the Effects of Shot-noise in Contact Holes Fabricated by E-beam Lithography
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Cited by 1 •
2017
Uniformly sized nanoparticles can remove fluctuations in contact hole dimensions patterned in poly(methyl methacrylate) (PMMA) photoresist films by electron beam (E-beam) lithography. The process involves electrostatic funneling to center and deposit nanoparticles in contact holes, followed by photoresist reflow and plasma- and wet-etching steps.
10:06
An Introduction to Learning and Memory
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2023
Learning is the process of acquiring new information and memory is the retention or storage of that information. Different types of learning, such as non-associative and associative learning, and different types of memory, such as long-term and short-term memory, have been associated with human behaviors. Studying these components in detail helps behavioral scientists understand the neural mechanisms behind these two complex phenomena. JoVE's overview on learning and memory introduces common...
12:06
Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
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Cited by 3 •
2023
This article explains how to use simulation-supervised machine learning for analyzing mitochondria morphology in fluorescence microscopy images of fixed cells.
06:02
Visual Statistical Learning
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2023
Source: Laboratory of Jonathan Flombaum—Johns Hopkins University The visual environment contains massive amounts of information involving the relations between objects in space and time; certain objects are more likely to appear in the vicinity of other objects. Learning these regularities can support a wide array of visual processing, including object recognition. Unsurprisingly, then, humans appear to learn these regularities automatically, quickly, and without conscious awareness. The name...
11:18
Quantifying Learning in Young Infants: Tracking Leg Actions During a Discovery-learning Task
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Cited by 17 •
2015
A method is described in which 3-4 month old infants learn a task by discovery and their leg movements are captured to quantify the learning process.
FAQs
Transferable representations provide a reusable starting point learned during prior training, so a new engineering task does not need to begin from raw, task-specific information. The available labeled examples then guide prediction for the new category or task. This mechanism is useful when collecting measurements and labels for every specialized system would be costly or slow.
These mechanisms provide different ways to use the small labeled example set. Similarity compares the new case with learned information, prompts guide the model toward the intended task, and parameter updates adapt the model itself. Their common purpose is to connect limited new-task evidence with capabilities developed during prior training.
Prior training supplies transferable knowledge or adaptation strategies before the model encounters the new engineering task. As a result, the limited examples can be used for generalization rather than for building the entire predictive capability from scratch. This is particularly important for specialized systems, where obtaining extensive labeled measurements may be impractical.
The workflow begins with a model that has learned transferable representations or task-adaptation strategies. Engineers then provide the small labeled set for the new task or category and apply a suitable mechanism, such as similarity, prompts, or parameter updates. The adapted system can then produce predictions for new cases using the available task-specific evidence.
It is most useful when labeled measurements are scarce or expensive to obtain, particularly in specialized systems. Engineers can consider it for tasks such as classification, fault diagnosis, inspection, or control when assembling a large task-specific dataset would delay development. Its efficient adaptation can support faster deployment and more timely data-driven decisions.
Few-shot Learning can support several forms of engineering decision-making, including identifying faults, analyzing inspection cases, and assisting control tasks. These uses extend beyond assigning categories because the method can adapt to different task types with limited labeled evidence. The expected practical benefit is shorter development cycles while retaining useful predictive support in data-constrained settings.