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• Learning to Transfer Knowledge Through Embedding Spaces

Learning to Transfer Knowledge Through Embedding Spaces

• Keynote speaker : Mohammad Rostami ( Pennsylvania University, USA)
• Date: 2020 April 23

Cross-Domain Knowledge Transfer

Cross-domain knowledge transfer is the process of applying knowledge learned from one domain to another related domain. It involves leveraging the existing knowledge learned from a source domain to improve the learning process in a target domain. The idea is to use the shared features or structures between the domains to transfer knowledge.

For example, suppose we have a dataset of images of animals, and we have trained a deep learning model to classify these animals. Now, suppose we want to train a model to classify images of cars. Since the two domains are related, we can leverage the knowledge learned from the animal dataset to improve the learning process in the car dataset. We can achieve this by using transfer learning through embedding spaces.

Transfer learning through embedding spaces involves training a model on a large, diverse dataset (source domain) and using the learned embedding space to train a model on a smaller, more specific dataset (target domain). The embedding space is a compressed representation of the data that captures the relevant features of the data.

In the above example, we can use the pre-trained model on the animal dataset to learn an embedding space that captures the features of the images. Then, we can use this embedding space to train a model on the car dataset. By doing so, the model can leverage the knowledge learned from the animal dataset to improve the learning process on the car dataset.

Another example of cross-domain knowledge transfer is the use of pre-trained word embeddings. Word embeddings are a popular way to represent words as vectors, where each dimension of the vector captures a different aspect of the word’s meaning. Pre-trained word embeddings are trained on large, diverse datasets and can be used to improve the learning process in various NLP tasks, such as sentiment analysis, text classification, and machine translation. By using pre-trained word embeddings, the model can leverage the knowledge learned from the source domain to improve the learning process in the target domain.

Cross-Task Knowledge Transfer

Cross-task knowledge transfer refers to the process of transferring knowledge learned from one task to another related or unrelated task. In other words, it involves using the knowledge acquired from solving one task to improve performance on another task.

One popular approach to cross-task knowledge transfer is through the use of transfer learning. Transfer learning involves using pre-trained models to initialize the weights of a neural network for a new task. The pre-trained model is usually trained on a large dataset for a similar or related task.

For example, a pre-trained model that was initially trained on a large image dataset to perform image classification can be fine-tuned for a new task such as object detection. The pre-trained model has already learned useful features from the image dataset, and this knowledge can be transferred to the new task, which can reduce the training time and improve the performance of the model.

Another example is the use of transfer learning in natural language processing (NLP) tasks. A pre-trained language model like BERT (Bidirectional Encoder Representations from Transformers) can be fine-tuned on a new task such as sentiment analysis or question-answering, which allows the model to learn from the specific task’s data and improve its performance.

In conclusion, cross-task knowledge transfer is a powerful approach that can help to reduce training time and improve the performance of models on new tasks by transferring knowledge learned from a related or unrelated task.