What are zero-shot learning methods?
Zero-shot learning (ZSL) is a machine learning approach in which a model learns to categorize unseen classes by using the relationships between seen and unseen classes based on semantic descriptions, attributes, or any other prior knowledge.
Note: In case you want to gain a comprehensive understanding of zero-shot learning before exploring its methodologies, please have a look at this Answer.
Taxonomy of ZSL methods
Zero-shot learning methods can be categorized into the following taxonomy based on different characteristics and approaches:
Attribute-based methods
Embedding-based methods
Knowledge graph-based methods
Generative methods
1. Attribute-based methods
The attribute-based methods rely on predefined attributes such as visual properties, textual features, or other relevant characteristics associated with each class. The model learns to correlate these properties with the appropriate class labels during training. At inference time, the model uses these attributes to classify unseen classes by matching their attributes to the learned representations.
Workflow
Here is a diagram demonstrating the workflow of attribute-based methods in zero-shot learning:
Algorithms
Attribute-based methods in zero-shot learning can be implemented using various algorithms. Here are a few examples:
Skip-Gram, Word2Vec, or GloVe can be used to learn attribute embeddings from textual descriptions or semantic relationships.
Semantic autoencoders or semantic compositional networks are commonly used to learn attribute-based models.
Label embedding trees or consistency-based semi-supervised embedding learn attribute label embeddings by mapping attributes to shared space.
Canonical correlation analysis or joint embedding is used to learn a common space where attributes, visual features, and textual descriptions are aligned.
Applications
Attribute-based methods in zero-shot learning have several real-world applications across various domains. Here are a few examples:
They can be applied to image classification tasks where the model is trained to recognize objects based on their attributes.
They can be used for product recommendation systems in e-commerce.
They can be applied to visual surveillance systems, such as identifying and tracking objects or individuals based on their attributes.
They have potential applications in medical diagnosis.
2. Embedding-based methods
In embedding-based methods, each class is represented by an embedding vector, typically learned using auxiliary data or semantic links between classes. The embedding vectors capture the similarity between different classes based on their underlying characteristics. During inference, the model projects the input data into the same embedding space and performs classification based on the nearest neighbor or similarity measures.
Workflow
Here is a diagram elaborating the workflow of embedding-based methods in zero-shot learning:
Algorithms
There are several algorithms used in embedding-based methods of zero-shot learning. Here are some commonly employed algorithms:
Word2Vec is a popular algorithm used to generate word embeddings.
FastText is an extension of Word2Vec that incorporates subword information.
Graph convolutional networks can be used to learn embeddings by considering semantic relationships between classes.
Applications
Embedding-based methods in zero-shot learning have various real-world applications across different domains. Some of the notable applications include:
These techniques can be used for anomaly detection in various domains, such as fraud detection and network intrusion detection.
They facilitate knowledge transfer between related but different domains.
They can enhance the capabilities of virtual assistants.
They can assist in medical diagnosis by leveraging learned representations of symptoms, diseases, or medical records.
3. Knowledge graph-based methods
The knowledge graph-based method uses structured knowledge graphs to represent the relationships between different classes. The model learns to reason and generalize based on the connections between seen and unseen classes. The knowledge graph can contain hierarchical information, semantic relationships, or any other relevant information that helps infer the class labels of unseen examples.
Workflow
Here is a diagram depicting the workflow of knowledge graph-based methods in zero-shot learning:
Algorithms
Several algorithms can be used for knowledge graph-based methods in zero-shot learning. Here are a few notable ones:
Graph convolutional networks can learn representations that incorporate both local and global contexts.
TransE, TransR, and ComplEx can map entities and relations into a continuous vector space, allowing for reasoning and inference about unseen entities and relations.
Graph attention networks are attention-based models that can operate on graph-structured data.
Applications
Knowledge graph-based methods in zero-shot learning have various real-world applications across different domains. Here are some examples:
They can be employed for object recognition, scene understanding, and visual question-answering tasks.
They can assist in drug discovery by predicting the properties, interactions, and side effects of compounds that have not been experimentally tested.
They can be applied to detection systems to detect novel or unseen fraud patterns.
They can help analyze social networks by predicting missing or hidden links between users or entities.
4. Generative methods
The generative methods involve generating synthetic data or prototypes for unseen classes. The model is trained to generate data samples representative of each class. During inference, the model compares the input data with the generated prototypes and assigns the class label based on similarity or distance measures.
Workflow
Here is a diagram explaining the workflow of generative methods in zero-shot learning:
Algorithms
There are several algorithms used in generative methods for zero-shot learning. Here are a few prominent ones:
Generative adversarial networks are a popular generative model that consists of two components: a generator and a discriminator.
Variational autoencoders are another popular generative model that combines an encoder and a decoder.
Semantic autoencoders combine a traditional autoencoder with an attribute-based encoder.
Applications
Generative methods in zero-shot learning have several real-world applications across various domains. Here are a few examples:
They can be useful in novel content generation.
These methods can aid in zero-shot text classification.
They can facilitate zero-shot retrieval across different modalities, such as text-to-image or image-to-text retrieval.
They can be useful in object recognition scenarios, specifically when dealing with rare or novel objects.
Note: The flowchart diagrams shown above describe a general workflow for each approach in zero-shot learning. However, remember that each strategy may have different implementations and modifications.
How to choose a ZSL method?
When choosing a zero-shot learning method, we should consider the following factors:
Available data: We must first determine the type and quantity of data for the seen and unseen classes. Some approaches may need annotated attribute information or pre-trained embeddings, while others require access to auxiliary data or knowledge graphs.
Domain knowledge: We should examine the availability of domain-specific knowledge or prior knowledge about unknown classes. When clear attribute descriptions are available, attribute-based approaches perform well, but embedding-based methods can use semantic linkages or similarities across classes.
Computational requirements: We must additionally consider the available computing resources and the required inference speed because different zero-shot learning approaches have varied computational difficulties. Depending on the dataset's amount and the model's complexity, certain approaches may need more processing than others.
Performance trade-offs: We need to evaluate the trade-offs between accuracy and interpretability. Some approaches achieve greater accuracy but produce less interpretable findings, while others provide more explainable classification conclusions at the expense of some accuracy.
Note: We can use the hybrid approach by combining the multiple methods such as attribute-based and semantic embedding-based approaches to leverage the strengths of different methods and improve zero-shot learning performance.
