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 9951–10000 of 10580 papers

TitleStatusHype
FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination—0
Federated Contrastive Representation Learning with Feature Fusion and Neighborhood Matching—0
Federated Graph Representation Learning using Self-Supervision—0
Federated Learning with MMD-based Early Stopping for Adaptive GNSS Interference Classification—0
Federated Model Heterogeneous Matryoshka Representation Learning—0
Federated Representation Learning for Automatic Speech Recognition—0
Federated Representation Learning via Maximal Coding Rate Reduction—0
Towards Communication-Efficient and Privacy-Preserving Federated Representation Learning—0
Federated Self-supervised Learning for Heterogeneous Clients—0
Federated Training of Dual Encoding Models on Small Non-IID Client Datasets—0
Federated Unsupervised Representation Learning—0
Federated User Representation Learning—0
Federated Variational Learning for Anomaly Detection in Multivariate Time Series—0
Federated Word2Vec: Leveraging Federated Learning to Encourage Collaborative Representation Learning—0
FedGRec: Dynamic Spatio-Temporal Federated Graph Learning for Secure and Efficient Cross-Border Recommendations—0
PVLR: Prompt-driven Visual-Linguistic Representation Learning for Multi-Label Image Recognition—0
FedLog: Personalized Federated Classification with Less Communication and More Flexibility—0
FedMKGC: Privacy-Preserving Federated Multilingual Knowledge Graph Completion—0
Pykg2vec: A Python Library for Knowledge Graph Embedding—0
FedRSClip: Federated Learning for Remote Sensing Scene Classification Using Vision-Language Models—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated Learning—0
A Cross-Level Information Transmission Network for Predicting Phenotype from New Genotype: Application to Cancer Precision Medicine—0
Fermi-Bose Machine achieves both generalization and adversarial robustness—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Few-shot Classification with Hypersphere Modeling of Prototypes—0
Semantic Data Set Construction from Human Clustering and Spatial Arrangement—0
Deep Representation Learning with an Information-theoretic Loss—0
Personalized Multi-task Training for Recommender System—0
Few-Shot Learning on Graphs—0
QK Iteration: A Self-Supervised Representation Learning Algorithm for Image Similarity—0
Few-Shot Learning via Learning the Representation, Provably—0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images—0
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
Self-supervised Learning for Pre-Training 3D Point Clouds: A Survey—0
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion—0
Semantic decoupled representation learning for remote sensing image change detection—0
Field-aware Variational Autoencoders for Billion-scale User Representation Learning—0
Fill in the Gap! Combining Self-supervised Representation Learning with Neural Audio Synthesis for Speech Inpainting—0
Fill the Gap: Quantifying and Reducing the Modality Gap in Image-Text Representation Learning—0
Quality-Aware Prototype Memory for Face Representation Learning—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
Finding Similar Exercises in Retrieval Manner—0
Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications—0
Finding the Trigger: Causal Abductive Reasoning on Video Events—0
FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction—0
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive 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