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
Improving Clinical Predictions through Unsupervised Time Series Representation Learning—0
AdaFedFR: Federated Face Recognition with Adaptive Inter-Class Representation Learning—0
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding—0
Bootstrap Your Own Correspondences—0
Adversarial Classifier for Imbalanced Problems—0
Membership-Mappings for Data Representation Learning: Measure Theoretic Conceptualization—0
Memory-Augmented Attribute Manipulation Networks for Interactive Fashion Search—0
Deep Residual Hashing—0
Bootstrap Representation Learning for Segmentation on Medical Volumes and Sequences—0
Improving BERT-based Query-by-Document Retrieval with Multi-Task Optimization—0
Deep Representation Learning with Part Loss for Person Re-Identification—0
Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach—0
Better Pre-Training by Reducing Representation Confusion—0
Improve Supervised Representation Learning with Masked Image Modeling—0
Message passing all the way up—0
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective—0
Improvements to Self-Supervised Representation Learning for Masked Image Modeling—0
Deep Representation Learning of Tissue Metabolome and Computed Tomography Images Annotates Non-invasive Classification and Prognosis Prediction of NSCLC—0
Meta-Causal Feature Learning for Out-of-Distribution Generalization—0
Meta Distant Transfer Learning for Pre-trained Language Models—0
MetaDT: Meta Decision Tree with Class Hierarchy for Interpretable Few-Shot Learning—0
Multi-modal Causal Structure Learning and Root Cause Analysis—0
Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables—0
Meta-free few-shot learning via representation learning with weight averaging—0
Multimodal Contrastive Training for Visual Representation Learning—0
Deep Representation Learning of Patient Data from Electronic Health Records (EHR): A Systematic Review—0
Sample-Efficient Linear Representation Learning from Non-IID Non-Isotropic Data—0
Meta-learning Transferable Representations with a Single Target Domain—0
Improved Structural Discovery and Representation Learning of Multi-Agent Data—0
Meta Multi-Task Learning for Sequence Modeling—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
COD: Learning Conditional Invariant Representation for Domain Adaptation Regression—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
Bootstrapping Disjoint Datasets for Multilingual Multimodal Representation Learning—0
MetaSegNet: Metadata-collaborative Vision-Language Representation Learning for Semantic Segmentation of Remote Sensing Images—0
Improved Self-Supervised Multilingual Speech Representation Learning Combined with Auxiliary Language Information—0
MetaViewer: Towards A Unified Multi-View Representation—0
Improved Sample Complexity for Reward-free Reinforcement Learning under Low-rank MDPs—0
MolTRES: Improving Chemical Language Representation Learning for Molecular Property Prediction—0
METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis—0
Improved Representation Learning Through Tensorized Autoencoders—0
Deep Representation Learning in Speech Processing: Challenges, Recent Advances, and Future Trends—0
Improved Representation Learning for Question Answer Matching—0
Metric Learning on Temporal Graphs via Few-Shot Examples—0
Metric Learning vs Classification for Disentangled Music Representation Learning—0
Improved Representation Learning for Predicting Commonsense Ontologies—0
Deep representation learning: Fundamentals, Perspectives, Applications, and Open Challenges—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