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 6051–6100 of 10580 papers

TitleStatusHype
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation—0
Cluster Analysis with Deep Embeddings and Contrastive Learning—0
Generalized Supervised Contrastive Learning—0
ACT-JEPA: Joint-Embedding Predictive Architecture Improves Policy Representation Learning—0
Looking Similar Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning—0
Looking Similar, Sounding Different: Leveraging Counterfactual Cross-Modal Pairs for Audiovisual Representation Learning—0
Memory-Augmented Attribute Manipulation Networks for Interactive Fashion Search—0
Looking Beyond Single Images for Contrastive Semantic Segmentation Learning—0
dynnode2vec: Scalable Dynamic Network Embedding—0
DynaSubVAE: Adaptive Subgrouping for Scalable and Robust OOD Detection—0
SynCoBERT: Syntax-Guided Multi-Modal Contrastive Pre-Training for Code Representation—0
Long Short View Feature Decomposition via Contrastive Video Representation Learning—0
Long-horizon video prediction using a dynamic latent hierarchy—0
LoNe Sampler: Graph node embeddings by coordinated local neighborhood sampling—0
Message passing all the way up—0
Dynamic Spiking Framework for Graph Neural Networks—0
Logographic Information Aids Learning Better Representations for Natural Language Inference—0
Dynamic Spectrum Matching with One-shot Learning—0
CLOVER: Context-aware Long-term Object Viewpoint- and Environment- Invariant Representation Learning—0
Meta Distant Transfer Learning for Pre-trained Language Models—0
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning—0
Neural Latent Space Model for Dynamic Networks and Temporal Knowledge Graphs—0
Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior Knowledge—0
Meta-free few-shot learning via representation learning with weight averaging—0
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification—0
Dynamic Scenario Representation Learning for Motion Forecasting with Heterogeneous Graph Convolutional Recurrent Networks—0
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data—0
Meta-learning Transferable Representations with a Single Target Domain—0
Closing the Gap: Domain Adaptation from Explicit to Implicit Discourse Relations—0
LoDisc: Learning Global-Local Discriminative Features for Self-Supervised Fine-Grained Visual Recognition—0
LoCo: Local Contrastive Representation Learning—0
Associative Learning Mechanism for Drug-Target Interaction Prediction—0
Meta-Path-Free Representation Learning on Heterogeneous Networks—0
Meta-path Free Semi-supervised Learning for Heterogeneous Networks—0
Meta-Principled Family of Hyperparameter Scaling Strategies—0
Meta-Reinforcement Learning Based on Self-Supervised Task Representation Learning—0
Local-to-Global Self-Supervised Representation Learning for Diabetic Retinopathy Grading—0
MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images—0
Local Structure-aware Graph Contrastive Representation Learning—0
MetaViewer: Towards A Unified Multi-View Representation—0
Local Manifold Augmentation for Multiview Semantic Consistency—0
Localized Graph Collaborative Filtering—0
Localized Contrastive Learning on Graphs—0
Localized and Balanced Efficient Incomplete Multi-view Clustering—0
Dynamic Network Embedding Survey—0
Associative Compression Networks for Representation Learning—0
A Generalized Model for Multidimensional Intransitivity—0
Active Representation Learning for General Task Space with Applications in Robotics—0
Localization-Aware Multi-Scale Representation Learning for Repetitive Action Counting—0
Locality-Promoting Representation Learning—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