SOTAVerified

Representation Learning

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:

( Image credit: Visualizing and Understanding Convolutional Networks )

Papers

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Showing 4251–4300 of 10580 papers

TitleStatusHype
Inductive Topic Variational Graph Auto-Encoder for Text Classification—0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
A Free-Energy Principle for Representation Learning—0
Active Multimodal Distillation for Few-shot Action Recognition—0
Dual-Neighborhood Deep Fusion Network for Point Cloud Analysis—0
Dual Motion GAN for Future-Flow Embedded Video Prediction—0
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization—0
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks—0
Dual-Modality Representation Learning for Molecular Property Prediction—0
Dynamic Traceback Learning for Medical Report Generation—0
Classifying Diagrams and Their Parts using Graph Neural Networks: A Comparison of Crowd-Sourced and Expert Annotations—0
Inductive Representation Learning in Large Attributed Graphs—0
Inferential SIR-GN: Scalable Graph Representation Learning—0
A Simple Imitation Learning Method via Contrastive Regularization—0
Inductive-Biases for Contrastive Learning of Disentangled Representations—0
DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awareness—0
Classification of developmental and brain disorders via graph convolutional aggregation—0
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning—0
Dual Graph Representation Learning—0
Dual Graph Complementary Network—0
Active metric learning and classification using similarity queries—0
A Simple General Method for Detecting Textual Adversarial Examples—0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
Inductive Graph Representation Learning with Quantum Graph Neural Networks—0
Dual-Granularity Contrastive Learning for Session-based Recommendation—0
Classes Are Not Equal: An Empirical Study on Image Recognition Fairness—0
Dual Encoder-Decoder based Generative Adversarial Networks for Disentangled Facial Representation Learning—0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
A Simple Framework for Uncertainty in Contrastive Learning—0
Class-aware and Augmentation-free Contrastive Learning from Label Proportion—0
A Simple Framework for Open-Vocabulary Zero-Shot Segmentation—0
TraceGrad: a Framework Learning Expressive SO(3)-equivariant Non-linear Representations for Electronic-Structure Hamiltonian Prediction—0
Causal Representation Learning for Context-Aware Face Transfer—0
Semantic Implicit Neural Scene Representations With Semi-Supervised Training—0
Dual Contrastive Learning for Spatio-temporal Representation—0
Dual Contradistinctive Generative Autoencoder—0
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior—0
CLAP: Unsupervised 3D Representation Learning for Fusion 3D Perception via Curvature Sampling and Prototype Learning—0
Dual-Channel Multiplex Graph Neural Networks for Recommendation—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing—0
DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation—0
Indicative Image Retrieval: Turning Blackbox Learning into Grey—0
In-Distribution and Out-of-Distribution Self-supervised ECG Representation Learning for Arrhythmia Detection—0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection—0
A Framework for Generalizing Graph-based Representation Learning Methods—0
Dropping Convexity for More Efficient and Scalable Online Multiview Learning—0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
Dropout Training for SVMs with Data Augmentation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SciNCLAvg.81.8—Unverified
2SPECTERAvg.80—Unverified
3CiteomaticAvg.76—Unverified
4Sci-DeCLUTRAvg.66.6—Unverified
5SciBERTAvg.59.6—Unverified
6BioBERTAvg.58.8—Unverified
7CiteBERTAvg.58.8—Unverified
#ModelMetricClaimedVerifiedStatus
1top_model_weights_with_3d_21:1 Accuracy0.75—Unverified
#ModelMetricClaimedVerifiedStatus
1Resnet 18Accuracy (%)97.05—Unverified
#ModelMetricClaimedVerifiedStatus
1Morphological NetworkAccuracy97.3—Unverified
#ModelMetricClaimedVerifiedStatus
1Max Margin ContrastiveSilhouette Score0.56—Unverified