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 5001–5050 of 10580 papers

TitleStatusHype
FVD: A new Metric for Video Generation—0
Fuzzy Rule-based Differentiable Representation Learning—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
Future Prediction Can be a Strong Evidence of Good History Representation in Partially Observable Environments—0
Controlling Computation versus Quality for Neural Sequence Models—0
Fus-MAE: A cross-attention-based data fusion approach for Masked Autoencoders in remote sensing—0
Controlled Text Generation Using Dictionary Prior in Variational Autoencoders—0
Automatic coding of students' writing via Contrastive Representation Learning in the Wasserstein space—0
AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning—0
FusionViT: Hierarchical 3D Object Detection via LiDAR-Camera Vision Transformer Fusion—0
Fusion of Diffusion Weighted MRI and Clinical Data for Predicting Functional Outcome after Acute Ischemic Stroke with Deep Contrastive Learning—0
ControlVAE: Controllable Variational Autoencoder—0
FUSE: Measure-Theoretic Compact Fuzzy Set Representation for Taxonomy Expansion—0
Fuse Local and Global Semantics in Representation Learning—0
Controllable Invariance through Adversarial Feature Learning—0
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation—0
Controllable Chest X-Ray Report Generation from Longitudinal Representations—0
Fundamental Limits and Tradeoffs in Invariant Representation Learning—0
Controllable Augmentations for Video Representation Learning—0
Automatically Discovering Novel Visual Categories with Self-supervised Prototype Learning—0
Function space analysis of deep learning representation layers—0
Functional Transparency for Structured Data: a Game-Theoretic Approach—0
Control False Negative Instances In Contrastive Learning To ImproveLong-tailed Item Categorization—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Functional2Structural: Cross-Modality Brain Networks Representation Learning—0
FUNCK: Information Funnels and Bottlenecks for Invariant Representation Learning—0
Control-Aware Representations for Model-based Reinforcement Learning—0
Contributions to Representation Learning with Graph Autoencoders and Applications to Music Recommendation—0
A Metric Learning Approach to Misogyny Categorization—0
Adaptive Text Recognition through Visual Matching—0
Adaptive Structural Similarity Preserving for Unsupervised Cross Modal Hashing—0
A Class-Aware Representation Refinement Framework for Graph Classification—0
Secure Embedding Aggregation for Federated Representation Learning—0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
Contrast-reconstruction Representation Learning for Self-supervised Skeleton-based Action Recognition—0
Automated Learning of Semantic Embedding Representations for Diffusion Models—0
Contrast Phase Classification with a Generative Adversarial Network—0
Contrastive Word Embedding Learning for Neural Machine Translation—0
Automated Label Generation for Time Series Classification with Representation Learning: Reduction of Label Cost for Training—0
From superposition to sparse codes: interpretable representations in neural networks—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
From Prototypes to General Distributions: An Efficient Curriculum for Masked Image Modeling—0
Contrastive Video-Language Learning with Fine-grained Frame Sampling—0
From Pixel to Slide image: Polarization Modality-based Pathological Diagnosis Using Representation Learning—0
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba—0
From Millions of Tweets to Actionable Insights: Leveraging LLMs for User Profiling—0
Contrastive Unsupervised Learning of World Model with Invariant Causal Features—0
Contrastive Unsupervised Learning for Speech Emotion Recognition—0
Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation—0
From Local Binary Patterns to Pixel Difference Networks for Efficient Visual 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