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 6401–6450 of 10580 papers

TitleStatusHype
Learning When the Concept Shifts: Confounding, Invariance, and Dimension Reduction—0
Multi-modal Alignment using Representation Codebook—0
Multimodal and self-supervised representation learning for automatic gesture recognition in surgical robotics—0
Multi-modal Causal Structure Learning and Root Cause Analysis—0
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior—0
CLAP: Unsupervised 3D Representation Learning for Fusion 3D Perception via Curvature Sampling and Prototype Learning—0
Learning What to Share: Leaky Multi-Task Network for Text Classification—0
Learning Weighted Representations for Generalization Across Designs—0
Multimodal Contrastive Learning via Uni-Modal Coding and Cross-Modal Prediction for Multimodal Sentiment Analysis—0
Dual-Channel Multiplex Graph Neural Networks for Recommendation—0
Learning Visual Representation from Human Interactions—0
Multimodal Contrastive Training for Visual Representation Learning—0
Learning Visual N-Grams from Web Data—0
Learning Visually Grounded Sentence Representations—0
Learning Visual Features from Large Weakly Supervised Data—0
Multimodal Deep Neural Networks using Both Engineered and Learned Representations for Biodegradability Prediction—0
Multimodal deep representation learning for quantum cross-platform verification—0
DTN: Deep Multiple Task-specific Feature Interactions Network for Multi-Task Recommendation—0
Self-supervised New Activity Detection in Sensor-based Smart Environments—0
A Framework for Understanding the Role of Morphology in Universal Dependency Parsing—0
Learning Visual Composition through Improved Semantic Guidance—0
Multimodal Generative Models for Compositional Representation Learning—0
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization—0
Learning Video Representations of Human Motion From Synthetic Data—0
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement—0
Multimodal Intelligence: Representation Learning, Information Fusion, and Applications—0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
Learning Video Representations from Textual Web Supervision—0
Learning Versatile 3D Shape Generation with Improved Auto-regressive Models—0
DSVAE: Interpretable Disentangled Representation for Synthetic Speech Detection—0
Learning Versatile 3D Shape Generation with Improved AR Models—0
Learning User Embeddings from Temporal Social Media Data: A Survey—0
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis—0
Dropping Convexity for More Efficient and Scalable Online Multiview Learning—0
Multi-Modal Molecular Representation Learning via Structure Awareness—0
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders—0
Multimodal Physiological Signals Representation Learning via Multiscale Contrasting for Depression Recognition—0
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
Multi-modal Relation Distillation for Unified 3D Representation Learning—0
Dropout Training for SVMs with Data Augmentation—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Learning Universal Representations from Word to Sentence—0
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning—0
CIRP: Cross-Item Relational Pre-training for Multimodal Product Bundling—0
Multi-modal Representation Learning for Cross-modal Prediction of Continuous Weather Patterns from Discrete Low-Dimensional Data—0
Circles are like Ellipses, or Ellipses are like Circles? Measuring the Degree of Asymmetry of Static and Contextual Embeddings and the Implications to Representation Learning—0
A Framework for Generalizing Graph-based Representation Learning Methods—0
Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting—0
Learning unbiased features—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