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 5251–5300 of 10580 papers

TitleStatusHype
Feature Representation Learning for NL2SQL Generation Based on Coupling and Decoupling—0
Feature Representation Learning for Click-through Rate Prediction: A Review and New Perspectives—0
Spectral-Aware Augmentation for Enhanced Graph Representation Learning—0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
Feature Projection for Improved Text Classification—0
Contrastive Classification and Representation Learning with Probabilistic Interpretation—0
Feature Normalization Prevents Collapse of Non-contrastive Learning Dynamics—0
Feature Matching Intervention: Leveraging Observational Data for Causal Representation Learning—0
Contrastive Brain Network Learning via Hierarchical Signed Graph Pooling Model—0
AlignMix: Improving representations by interpolating aligned features—0
Feature Interactive Representation for Point Cloud Registration—0
Feature Incay for Representation Regularization—0
Feature Imitating Networks—0
Feature-guided Neural Model Training for Supervised Document Representation Learning—0
Learning Video Representations using Contrastive Bidirectional Transformer—0
Feature Forgetting in Continual Representation Learning—0
Contrastive Attention Maps for Self-Supervised Co-Localization—0
Adaptive Normalized Representation Learning for Generalizable Face Anti-Spoofing—0
Feature Disentanglement of Robot Trajectories—0
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition—0
Feature-based Neural Language Model and Chinese Word Segmentation—0
Contrastive Approach to Prior Free Positive Unlabeled Learning—0
Feature-Based Lie Group Transformer for Real-World Applications—0
FEATURE-AUGMENTED HYPERGRAPH NEURAL NETWORKS—0
Feature Affinity based Pseudo Labeling for Semi-supervised Person Re-identification—0
Augment, Drop & Swap: Improving Diversity in LLM Captions for Efficient Music-Text Representation Learning—0
FDVTS's Solution for 2nd COV19D Competition on COVID-19 Detection and Severity Analysis—0
fCOP: Focal Length Estimation from Category-level Object Priors—0
FCOM: A Federated Collaborative Online Monitoring Framework via Representation Learning—0
Towards Better Few-Shot and Finetuning Performance with Forgetful Causal Language Models—0
FAVAE: SEQUENCE DISENTANGLEMENT USING IN- FORMATION BOTTLENECK PRINCIPLE—0
ContraReg: Contrastive Learning of Multi-modality Unsupervised Deformable Image Registration—0
Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices—0
Alignment and stability of embeddings: measurement and inference improvement—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Fast Node Embeddings: Learning Ego-Centric Representations—0
ContraCluster: Learning to Classify without Labels by Contrastive Self-Supervision and Prototype-Based Semi-Supervision—0
FastICARL: Fast Incremental Classifier and Representation Learning with Efficient Budget Allocation in Audio Sensing Applications—0
Continuous-time Graph Representation with Sequential Survival Process—0
Augmentation Invariant Manifold Learning—0
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems—0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
Fast and scalable learning of neuro-symbolic representations of biomedical knowledge—0
Fast and Sample Efficient Multi-Task Representation Learning in Stochastic Contextual Bandits—0
Continuous Histogram Loss: Beyond Neural Similarity—0
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs—0
Fast and Accurate Power Load Data Completion via Regularization-optimized Low-Rank Factorization—0
Fast Adaptive Federated Bilevel Optimization—0
Continuous Adversarial Text Representation Learning for Affective Recognition—0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning—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