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Related Concept Videos

Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Introduction to Learning01:18

Introduction to Learning

Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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Related Experiment Videos

Learning forward-compatible and domain-invariant representations for cross-domain few-shot class-incremental

Weidong Shi1, Xudong Yan1, Jiazheng Yuan2

  • 1School of Computer Science and Technology, Beijing Jiaotong University, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|May 11, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Cross-Domain Few-Shot Class-Incremental Learning (CDFSCIL), a new challenge for AI models learning new classes across different domains with limited data. The proposed FCDI framework effectively handles both data scarcity and domain shifts.

Keywords:
Cross-domain few-shot class-incremental learningDomain shiftFew-shot class-incremental learning

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computer Vision

Background:

  • Few-Shot Class-Incremental Learning (FSCIL) typically assumes data from a single domain.
  • Real-world applications often involve incremental classes from different domains than base classes.
  • Existing FSCIL methods struggle with domain shift and data scarcity.

Purpose of the Study:

  • Introduce Cross-Domain Few-Shot Class-Incremental Learning (CDFSCIL) as a more realistic challenge.
  • Develop a unified framework (FCDI) to address CDFSCIL.
  • Learn representations compatible with future classes and robust to domain shifts.

Main Methods:

  • Propose the Learning Forward-Compatible and Domain-Invariant Representations (FCDI) framework.
  • Construct a structured representation space for stable incremental learning.
  • Disentangle semantic and domain features for domain-invariant representations.
  • Employ diverse domain variations to enhance robustness.

Main Results:

  • FCDI significantly outperforms state-of-the-art approaches on the proposed CDFSCIL benchmark.
  • The method demonstrates strong performance on standard FSCIL tasks.
  • Experimental results validate the effectiveness of the FCDI framework.

Conclusions:

  • CDFSCIL presents a more challenging and realistic scenario for incremental learning.
  • The FCDI framework effectively addresses both data scarcity and domain shift in incremental learning.
  • FCDI learns robust, domain-invariant representations for improved few-shot class-incremental learning.