Representation Learning
3682 papers with code • 5 benchmarks • 9 datasets
Representation Learning is a process in machine learning where algorithms extract meaningful patterns from raw data to create representations that are easier to understand and process. These representations can be designed for interpretability, reveal hidden features, or be used for transfer learning. They are valuable across many fundamental machine learning tasks like image classification and retrieval.
Deep neural networks can be considered representation learning models that typically encode information which is projected into a different subspace. These representations are then usually passed on to a linear classifier to, for instance, train a classifier.
Representation learning can be divided into:
- Supervised representation learning: learning representations on task A using annotated data and used to solve task B
- Unsupervised representation learning: learning representations on a task in an unsupervised way (label-free data). These are then used to address downstream tasks and reducing the need for annotated data when learning news tasks. Powerful models like GPT and BERT leverage unsupervised representation learning to tackle language tasks.
More recently, self-supervised learning (SSL) is one of the main drivers behind unsupervised representation learning in fields like computer vision and NLP.
Here are some additional readings to go deeper on the task:
- Representation Learning: A Review and New Perspectives - Bengio et al. (2012)
- A Few Words on Representation Learning - Thalles Silva
( Image credit: Visualizing and Understanding Convolutional Networks )
Libraries
Use these libraries to find Representation Learning models and implementationsDatasets
Subtasks
- Disentanglement
- Graph Representation Learning
- Sentence Embeddings
- Network Embedding
- Network Embedding
- Sentence Embedding
- Knowledge Graph Embeddings
- Document Embedding
- Learning Word Embeddings
- Multilingual Word Embeddings
- Learning Semantic Representations
- Feature Upsampling
- Learning Network Representations
- Sentence Embeddings For Biomedical Texts
- Part-based Representation Learning
- Learning Representation Of Multi-View Data
- Learning Representation On Graph
Latest papers
Hypergraph Self-supervised Learning with Sampling-efficient Signals
Self-supervised learning (SSL) provides a promising alternative for representation learning on hypergraphs without costly labels.
Cluster-based Graph Collaborative Filtering
This model performs high-order graph convolution on cluster-specific graphs, which are constructed by capturing the multiple interests of users and identifying the common interests among them.
Knowledge-enhanced Visual-Language Pretraining for Computational Pathology
In this paper, we consider the problem of visual representation learning for computational pathology, by exploiting large-scale image-text pairs gathered from public resources, along with the domain specific knowledge in pathology.
Cross-Modal Self-Training: Aligning Images and Pointclouds to Learn Classification without Labels
Thereby we demonstrate that 2D vision language models such as CLIP can be used to complement 3D representation learning to improve classification performance without the need for expensive class annotations.
Contrastive Pretraining for Visual Concept Explanations of Socioeconomic Outcomes
This improves the model's interpretability as it enables the latent space of the model to associate urban concepts with continuous intervals of socioeconomic outcomes.
VideoSAGE: Video Summarization with Graph Representation Learning
We propose a graph-based representation learning framework for video summarization.
Masked Image Modeling as a Framework for Self-Supervised Learning across Eye Movements
To make sense of their surroundings, intelligent systems must transform complex sensory inputs to structured codes that are reduced to task-relevant information such as object category.
TSLANet: Rethinking Transformers for Time Series Representation Learning
Time series data, characterized by its intrinsic long and short-range dependencies, poses a unique challenge across analytical applications.
Representation Learning of Tangled Key-Value Sequence Data for Early Classification
To address this problem, we propose a novel method, i. e., Key-Value sequence Early Co-classification (KVEC), which leverages both inner- and inter-correlations of items in a tangled key-value sequence through key correlation and value correlation to learn a better sequence representation.
Adaptive Fair Representation Learning for Personalized Fairness in Recommendations via Information Alignment
The existing works often treat a fairness requirement, represented as a collection of sensitive attributes, as a hyper-parameter, and pursue extreme fairness by completely removing information of sensitive attributes from the learned fair embedding, which suffer from two challenges: huge training cost incurred by the explosion of attribute combinations, and the suboptimal trade-off between fairness and accuracy.