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 3101–3150 of 10580 papers

TitleStatusHype
Gaussian Splatting Feature Fields for (Privacy-Preserving) Visual Localization—0
A Hypergraph Neural Network Framework for Learning Hyperedge-Dependent Node Embeddings—0
Comprehending Knowledge Graphs with Large Language Models for Recommender Systems—0
Compound Tokens: Channel Fusion for Vision-Language Representation Learning—0
A Transformer Based Handwriting Recognition System Jointly Using Online and Offline Features—0
Adaptive Contextual Embedding for Robust Far-View Borehole Detection—0
Composition of Sentence Embeddings:Lessons from Statistical Relational Learning—0
Composition of Sentence Embeddings: Lessons from Statistical Relational Learning—0
Compositional Scene Representation Learning via Reconstruction: A Survey—0
Compositional Representation Learning for Brain Tumour Segmentation—0
Domain-invariant Clinical Representation Learning by Bridging Data Distribution Shift across EMR Datasets—0
A Hybrid Spiking-Convolutional Neural Network Approach for Advancing Machine Learning Models—0
Gaussian Masked Autoencoders—0
Compositional Network Embedding—0
Compositional Mixture Representations for Vision and Text—0
A Transferable General-Purpose Predictor for Neural Architecture Search—0
Compositionally Equivariant Representation Learning—0
Compositionality and Generalization in Emergent Languages—0
Personalized Multi-task Training for Recommender System—0
Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language—0
Dynamic Latent Separation for Deep Learning—0
Composable Generative Models—0
Composable Augmentation Encoding for Video Representation Learning—0
Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning—0
Accelerating Deep Learning with Millions of Classes—0
Component Analysis for Visual Question Answering Architectures—0
Complete-to-Partial 4D Distillation for Self-Supervised Point Cloud Sequence Representation Learning—0
AtmoRep: A stochastic model of atmosphere dynamics using large scale representation learning—0
A Hybrid Learning Scheme for Chinese Word Embedding—0
Complete Cross-triplet Loss in Label Space for Audio-visual Cross-modal Retrieval—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Adaptive Audio-Visual Speech Recognition via Matryoshka-Based Multimodal LLMs—0
Competitive Learning Enriches Learning Representation and Accelerates the Fine-tuning of CNNs—0
Competing Mutual Information Constraints with Stochastic Competition-based Activations for Learning Diversified Representations—0
Learning Blended, Precise Semantic Program Embeddings—0
Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?—0
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting—0
GCC: Generative Calibration Clustering—0
CORL: Compositional Representation Learning for Few-Shot Classification—0
Comparison of Self-Supervised Speech Pre-Training Methods on Flemish Dutch—0
Comparison of Representations of Named Entities for Document Classification—0
Comparison of Representation Learning Techniques for Tracking in time resolved 3D Ultrasound—0
Adaptive Adversarial Multi-task Representation Learning—0
Comparing Trajectory and Vision Modalities for Verb Representation—0
A Holistically-Guided Decoder for Deep Representation Learning with Applications to Semantic Segmentation and Object Detection—0
Comparing Reconstruction- and Contrastive-based Models for Visual Task Planning—0
Comparing Data Sources and Architectures for Deep Visual Representation Learning in Semantics—0
A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning—0
A Causal Perspective of Stock Prediction Models—0
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison—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