Representation Learning
3690 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 with no code
Multi-view Graph Structural Representation Learning via Graph Coarsening
Specifically, we build three unique views, original, coarsening, and conversion, to learn a thorough structural representation.
DACAD: Domain Adaptation Contrastive Learning for Anomaly Detection in Multivariate Time Series
In this paper, we propose a novel Domain Adaptation Contrastive learning for Anomaly Detection in multivariate time series (DACAD) model to address this issue by combining UDA and contrastive representation learning.
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection
Next, a shape-aware and a brightness-aware module are designed to improve the capacity for representation learning.
CORE: Data Augmentation for Link Prediction via Information Bottleneck
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains.
A Novel ICD Coding Framework Based on Associated and Hierarchical Code Description Distillation
To address these problems, we propose a novel framework based on associated and hierarchical code description distillation (AHDD) for better code representation learning and avoidance of improper code assignment. we utilize the code description and the hierarchical structure inherent to the ICD codes.
DRepMRec: A Dual Representation Learning Framework for Multimodal Recommendation
After obtaining separate behavior and modal representations, we design a Behavior-Modal Alignment Module (BMA) to align and fuse the dual representations to solve the misalignment problem.
Prompt-Driven Feature Diffusion for Open-World Semi-Supervised Learning
In this paper, we present a novel approach termed Prompt-Driven Feature Diffusion (PDFD) within a semi-supervised learning framework for Open World Semi-Supervised Learning (OW-SSL).
Improved Generalization Bounds for Communication Efficient Federated Learning
This paper focuses on reducing the communication cost of federated learning by exploring generalization bounds and representation learning.
Equivariant Spatio-Temporal Self-Supervision for LiDAR Object Detection
This can enable improved performance in downstream tasks that are equivariant to such transformations.
AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer
Recently, heterogeneous graph neural networks (HGNNs) have achieved impressive success in representation learning by capturing long-range dependencies and heterogeneity at the node level.