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 7401–7450 of 10580 papers

TitleStatusHype
Privacy-Preserving Adversarial Representation Learning in ASR: Reality or Illusion?—0
Anonymizing Sensor Data on the Edge: A Representation Learning and Transformation Approach—0
Causal Perception Inspired Representation Learning for Trustworthy Image Quality Assessment—0
A Representation Learning Approach to Animal Biodiversity Conservation—0
Adversarial Unsupervised Representation Learning for Activity Time-Series—0
Privacy-Preserving Machine Learning for Collaborative Data Sharing via Auto-encoder Latent Space Embeddings—0
Ablation Study to Clarify the Mechanism of Object Segmentation in Multi-Object Representation Learning—0
Learning Compositional Representations of Interacting Systems with Restricted Boltzmann Machines: Comparative Study of Lattice Proteins—0
Learning Compact Features via In-Training Representation Alignment—0
Privacy-Preserving Representation Learning on Graphs: A Mutual Information Perspective—0
Disentangling Geometric Deformation Spaces in Generative Latent Shape Models—0
Privacy-preserving Voice Analysis via Disentangled Representations—0
Learning Color Representations for Low-Light Image Enhancement—0
Private-Shared Disentangled Multimodal VAE for Learning of Hybrid Latent Representations—0
Learning Chemical Reaction Representation with Reactant-Product Alignment—0
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models—0
Disentangling Factors of Variation Using Few Labels—0
Learning Causal Response Representations through Direct Effect Analysis—0
Disentangling Factors of Variations Using Few Labels—0
Probabilistic Lexical Manifold Construction in Large Language Models via Hierarchical Vector Field Interpolation—0
Causal Machine Learning for Healthcare and Precision Medicine—0
Probabilistic Multimodal Representation Learning—0
Are Music Foundation Models Better at Singing Voice Deepfake Detection? Far-Better Fuse them with Speech Foundation Models—0
Probabilistic World Modeling with Asymmetric Distance Measure—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
Probing Contextual Language Models for Common Ground with Visual Representations—0
Learning Causal Representations from General Environments: Identifiability and Intrinsic Ambiguity—0
Learning Causal Representation for Training Cross-Domain Pose Estimator via Generative Interventions—0
Learning Canonical F-Correlation Projection for Compact Multiview Representation—0
Learning Cancer Outcomes from Heterogeneous Genomic Data Sources: An Adversarial Multi-task Learning Approach—0
Disentangling Disentangled Representations: Towards Improved Latent Units via Diffusion Models—0
Learning by Watching: A Review of Video-based Learning Approaches for Robot Manipulation—0
Learning by Reconstruction Produces Uninformative Features For Perception—0
Proceedings of the 2nd Workshop on Representation Learning for NLP—0
Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019)—0
Proceedings of the 5th Workshop on Representation Learning for NLP—0
Proceedings of The Third Workshop on Representation Learning for NLP—0
Learning by Aligning Videos in Time—0
Disentangling by Partitioning: A Representation Learning Framework for Multimodal Sensory Data—0
Product Knowledge Graph Embedding for E-commerce—0
Causal Machine Learning: A Survey and Open Problems—0
Are Hyperbolic Representations in Graphs Created Equal?—0
Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendation—0
PROFIT: A Specialized Optimizer for Deep Fine Tuning—0
Programming knowledge tracing based on heterogeneous graph representation—0
Learning Bilingual Projections of Embeddings for Vocabulary Expansion in Machine Translation—0
Learning Bias-Invariant Representation by Cross-Sample Mutual Information Minimization—0
Learning Better Visual Representations for Weakly-Supervised Object Detection Using Natural Language Supervision—0
Progressive growing of self-organized hierarchical representations for exploration—0
Disentangling Age and Identity with a Mutual Information Minimization Approach for Cross-Age Speaker Verification—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
6CiteBERTAvg.58.8—Unverified
7BioBERTAvg.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