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Latest Research Topics for One-Shot Learning

Latest Research Topics for One-Shot Learning

PhD Research Topics for One-Shot Learning

One-shot learning is a challenging process in the area of computer vision, and it learns information about the categorization or classification of any object using one or only a few training samples/images. One-shot learning is a specific type of few-shot learning and provides better performance even under limited resources. Several algorithms were developed for one-shot learning, such as Probabilistic models based on Bayesian learning, generative models using probability density functions, applying the transformation to images, memory augmented neural networks, meta-learning, and metric learning.

The most popular applications of one-shot learning are face recognition, verification and identification, object recognition and categorization, anomaly detection, and product recognition. Recent advancements in one-shot learning are active one-shot learning and domain adaptations such as adversarial network and reinforcement learning.

  • Deploying a traditional deep network for recognizing or classifying any tasks would require a large dataset. Procuring such a huge corpus is time-consuming and also demands human intervention, which makes it a costly and overwhelming task.

  • To encounter this issue, One-shot learning is an emerged technology that aims to categorize the new classes unseen in training set from one example of each new class where one annotated example is available for each class for prediction and classification.

  • Because of learning from just one sample per unique class, the One-shot learning algorithm is very beneficial for deep networks, which may suffer from overfitting.

  • One-shot learning has advantages over traditional deep networks, which require a lot of training samples and lack robustness due to their excessive domain-specific discriminators.

  • Recently, several key milestones have been achieved by one-shot learning. Some are handwriting recognition, vision and image classification, and drug discovery.