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 5501–5550 of 10580 papers

TitleStatusHype
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes—0
Practical Skills Demand Forecasting via Representation Learning of Temporal Dynamics—0
Cross-subject Action Unit Detection with Meta Learning and Transformer-based Relation Modeling—0
Relational representation learning with spike trains—0
Masked Autoencoders As Spatiotemporal LearnersCode2
Learning latent representations for operational nitrogen response rate prediction—0
Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical ImagingCode1
Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification—0
Self-Supervised Learning of Multi-Object Keypoints for Robotic Manipulation—0
A two-steps approach to improve the performance of Android malware detectors—0
SAMU-XLSR: Semantically-Aligned Multimodal Utterance-level Cross-Lingual Speech Representation—0
KGNN: Distributed Framework for Graph Neural Knowledge Representation—0
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibilityCode0
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation—0
An Empirical Investigation of Representation Learning for ImitationCode1
Real-centric Consistency Learning for Deepfake Detection—0
Clinical outcome prediction under hypothetical interventions -- a representation learning framework for counterfactual reasoning—0
Learning Representations for New Sound Classes With Continual Self-Supervised LearningCode1
Learning Lip-Based Audio-Visual Speaker Embeddings with AV-HuBERTCode2
Joint Representation Learning and Keypoint Detection for Cross-view Geo-localizationCode1
BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification—0
Voxel-wise Adversarial Semi-supervised Learning for Medical Image Segmentation—0
Representation learning with function call graph transformations for malware open set recognition—0
Unsupervised Representation Learning for 3D MRI Super Resolution with Degradation Adaptation—0
Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs—0
Simple and Effective Relation-based Embedding Propagation for Knowledge Representation LearningCode3
Unsupervised Driving Behavior Analysis using Representation Learning and Exploiting Group-based Training—0
Distribution-Aware Graph Representation Learning for Transient Stability Assessment of Power SystemCode1
Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations—0
AdaVAE: Exploring Adaptive GPT-2s in Variational Auto-Encoders for Language ModelingCode1
Accounting for the Sequential Nature of States to Learn Features for Reinforcement Learning—0
Entity-aware and Motion-aware Transformers for Language-driven Action Localization in VideosCode1
Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction PredictionCode1
Representation Learning for Context-Dependent Decision-Making—0
kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval—0
Group R-CNN for Weakly Semi-supervised Object Detection with PointsCode1
Hyperspectral Image Classification With Contrastive Graph Convolutional NetworkCode0
A deep representation learning speech enhancement method using β-VAE—0
Contrastive Supervised Distillation for Continual Representation LearningCode1
Scene Consistency Representation Learning for Video Scene SegmentationCode1
Robust Medical Image Classification from Noisy Labeled Data with Global and Local Representation Guided Co-training—0
From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More EffectiveCode1
CoDo: Contrastive Learning with Downstream Background Invariance for Detection—0
Surreal-GAN:Semi-Supervised Representation Learning via GAN for uncovering heterogeneous disease-related imaging patternsCode1
Multimodal Semi-Supervised Learning for Text RecognitionCode1
Deep Embedded Multi-View Clustering via Jointly Learning Latent Representations and Graphs—0
Representation Learning for Compressed Video Action Recognition via Attentive Cross-modal Interaction with Motion Enhancement—0
Functional2Structural: Cross-Modality Brain Networks Representation Learning—0
The NT-Xent loss upper bound—0
DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation—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