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 5151–5200 of 10580 papers

TitleStatusHype
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Contrastive Learning with Nasty Noise—0
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation—0
FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction—0
Author Name Disambiguation via Heterogeneous Network Embedding from Structural and Semantic Perspectives—0
All the attention you need: Global-local, spatial-channel attention for image retrieval—0
Finding the Trigger: Causal Abductive Reasoning on Video Events—0
Finding the Sparsest Vectors in a Subspace: Theory, Algorithms, and Applications—0
Finding Similar Exercises in Retrieval Manner—0
Spatiotemporal Adaptive Neural Network for Long-term Forecasting of Financial Time Series—0
Contrastive Learning Through Time—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
Fill the Gap: Quantifying and Reducing the Modality Gap in Image-Text Representation Learning—0
Fill in the Gap! Combining Self-supervised Representation Learning with Neural Audio Synthesis for Speech Inpainting—0
Field-aware Variational Autoencoders for Billion-scale User Representation Learning—0
Contrastive Learning on Multimodal Analysis of Electronic Health Records—0
A Uniform Representation Learning Method for OCT-based Fingerprint Presentation Attack Detection and Reconstruction—0
All-optical graph representation learning using integrated diffractive photonic computing units—0
Adaptive Prototypical Networks with Label Words and Joint Representation Learning for Few-Shot Relation Classification—0
2D LiDAR Map Prediction via Estimating Motion Flow with GRU—0
IN-Sight: Interactive Navigation through Sight—0
Hangul Fonts Dataset: a Hierarchical and Compositional Dataset for Investigating Learned Representations—0
Tag2Vec: Learning Tag Representations in Tag Networks—0
Contrastive Learning of Person-independent Representations for Facial Action Unit Detection—0
FFHR: Fully and Flexible Hyperbolic Representation for Knowledge Graph Completion—0
Contrastive Learning of Image Representations with Cross-Video Cycle-Consistency—0
A Unified View on Neural Message Passing with Opinion Dynamics for Social Networks—0
Few-shot Weakly-supervised Cybersecurity Anomaly Detection—0
Few-Shot Unsupervised Implicit Neural Shape Representation Learning with Spatial Adversaries—0
Learning Transferable Adversarial Robust Representations via Multi-view Consistency—0
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation—0
Few-Shot Meta Learning for Recognizing Facial Phenotypes of Genetic Disorders—0
Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images—0
Few-Shot Learning via Learning the Representation, Provably—0
Contrastive learning, multi-view redundancy, and linear models—0
A Unified Transformer-based Network for multimodal Emotion Recognition—0
Few-Shot Learning on Graphs—0
Deep Representation Learning with an Information-theoretic Loss—0
A Unified Representation Learning Strategy for Open Relation Extraction with Ranked List Loss—0
All-in-One Transferring Image Compression from Human Perception to Multi-Machine Perception—0
Few-shot Classification with Hypersphere Modeling of Prototypes—0
Contrastive Learning for Regression on Hyperspectral Data—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Contrastive Learning for Low Resource Machine Translation—0
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight—0
Fermi-Bose Machine achieves both generalization and adversarial robustness—0
Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems—0
UIILD: A Unified Interpretable Intelligent Learning Diagnosis Framework for Intelligent Tutoring Systems—0
Alleviating neighbor bias: augmenting graph self-supervise learning with structural equivalent positive samples—0
FedSA: A Unified Representation Learning via Semantic Anchors for Prototype-based Federated 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