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 5551–5600 of 10580 papers

TitleStatusHype
Towards Understanding How Transformers Learn In-context Through a Representation Learning Lens—0
In-Context Learning for Few-Shot Nested Named Entity Recognition—0
DIRL: Domain-Invariant Representation Learning for Sim-to-Real Transfer—0
Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing—0
Deep Within-Class Covariance Analysis for Robust Deep Audio Representation Learning—0
Learning Semantics: An Opportunity for Effective 6G Communications—0
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning—0
Brain Structure-Function Fusing Representation Learning using Adversarial Decomposed-VAE for Analyzing MCI—0
Deep Visual-Semantic Quantization for Efficient Image Retrieval—0
Incidence Networks for Geometric Deep Learning—0
In-bed Pressure-based Pose Estimation using Image Space Representation Learning—0
Learning Shape Representations for Clothing Variations in Person Re-Identification—0
Learning Smooth and Fair Representations—0
Learning Solving Procedure for Artificial Neural Network—0
Learning sound representations using trainable COPE feature extractors—0
Learning Sparse Latent Representations with the Deep Copula Information Bottleneck—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
Manifold-based Incomplete Multi-view Clustering via Bi-Consistency Guidance—0
Learning Spatiotemporal-Aware Representation for POI Recommendation—0
Learning spatio-temporal representations with temporal squeeze pooling—0
Deep video representation learning: a survey—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
MAMO: Masked Multimodal Modeling for Fine-Grained Vision-Language Representation Learning—0
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network—0
Improving Zero-shot Voice Style Transfer via Disentangled Representation Learning—0
Brain-aligning of semantic vectors improves neural decoding of visual stimuli—0
A Self-Adjusting Fusion Representation Learning Model for Unaligned Text-Audio Sequences—0
Learning State Representations via Temporal Cycle-Consistency Constraint in Model-Based Reinforcement Learning—0
Joint Debiased Representation Learning and Imbalanced Data Clustering—0
Learning strange attractors with reservoir systems—0
Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception—0
Improving Video Model Transfer With Dynamic Representation Learning—0
Deep Variational Multivariate Information Bottleneck -- A Framework for Variational Losses—0
Learning Structured Representations of Visual Scenes—0
Deep Variational Luenberger-type Observer for Stochastic Video Prediction—0
Improving VAE-based Representation Learning—0
Learning Subgoal Representations with Slow Dynamics—0
Learning Successor Features with Distributed Hebbian Temporal Memory—0
Improving Unsupervised Subword Modeling via Disentangled Speech Representation Learning and Transformation—0
MAML and ANIL Provably Learn Representations—0
Manifold-aware Representation Learning for Degradation-agnostic Image Restoration—0
Learning Target-oriented Dual Attention for Robust RGB-T Tracking—0
Learning Task-Agnostic Skill Bases to Uncover Motor Primitives in Animal Behaviors—0
Learning Task-Relevant Features via Contrastive Input Morphing—0
Brain-Driven Representation Learning Based on Diffusion Model—0
Chemical Property Prediction Under Experimental Biases—0
Learning telic-controllable state representations—0
BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks—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