Beginner Explanation
Imagine you learned how to ride a bicycle. Now, when you try to ride a scooter, you find it easier because you already know how to balance and steer from biking. This is knowledge transfer – using what you learned in one situation to help you in another similar situation. It’s like taking skills from one game and using them in another game that has some of the same rules.Technical Explanation
Knowledge transfer in machine learning refers to the ability of a model trained on one task to improve performance on a related but different task. This is often implemented through techniques like transfer learning, where a pre-trained model (e.g., a neural network trained on ImageNet) is fine-tuned on a smaller dataset for a specific task. For example, using a model trained for object detection to recognize specific types of objects in a different context. In Python, this can be done using libraries like TensorFlow or PyTorch: “`python from tensorflow.keras.applications import VGG16 from tensorflow.keras.models import Model # Load the VGG16 model pre-trained on ImageNet base_model = VGG16(weights=’imagenet’, include_top=False) # Add custom layers for the new task x = base_model.output x = Flatten()(x) # Add more layers as needed model = Model(inputs=base_model.input, outputs=x) # Freeze the layers of the base model for layer in base_model.layers: layer.trainable = False # Compile and train the model on the new dataset model.compile(optimizer=’adam’, loss=’categorical_crossentropy’, metrics=[‘accuracy’]) model.fit(new_data, new_labels) “`Academic Context
Knowledge transfer is a critical concept in cognitive psychology and education, as well as in machine learning. The theoretical underpinnings involve understanding how knowledge is structured and how it can be applied across different contexts. Research by Barnett and Ceci (2002) discusses the ‘transfer of learning’ and identifies factors that influence transfer, such as similarity between tasks and the learner’s prior knowledge. In the context of machine learning, the concept is often explored in transfer learning literature, with key papers including ‘A Survey on Transfer Learning’ by Pan and Yang (2010) and ‘Deep Learning for Transfer Learning’ by Yosinski et al. (2014), which provide insights into how neural networks can leverage pre-existing knowledge to improve performance in new domains.Code Examples
Example 1:
from tensorflow.keras.applications import VGG16
from tensorflow.keras.models import Model
# Load the VGG16 model pre-trained on ImageNet
base_model = VGG16(weights='imagenet', include_top=False)
# Add custom layers for the new task
x = base_model.output
x = Flatten()(x)
# Add more layers as needed
model = Model(inputs=base_model.input, outputs=x)
# Freeze the layers of the base model
for layer in base_model.layers:
layer.trainable = False
# Compile and train the model on the new dataset
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
model.fit(new_data, new_labels)Example 2:
layer.trainable = FalseExample 3:
from tensorflow.keras.applications import VGG16
from tensorflow.keras.models import Model
# Load the VGG16 model pre-trained on ImageNet
base_model = VGG16(weights='imagenet', include_top=False)Example 4:
from tensorflow.keras.models import Model
# Load the VGG16 model pre-trained on ImageNet
base_model = VGG16(weights='imagenet', include_top=False)
View Source: https://arxiv.org/abs/2511.16485v1