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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7151–7200 of 10580 papers

TitleStatusHype
3D Point Cloud Pre-training with Knowledge Distillation from 2D Images—0
How Fine-Tuning Allows for Effective Meta-Learning—0
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty—0
Unsupervised Deep Representation Learning and Few-Shot Classification of PolSAR Images—0
Deep Graph Generators: A Survey—0
PedHunter: Occlusion Robust Pedestrian Detector in Crowded Scenes—0
How Do Multilingual Encoders Learn Cross-lingual Representation?—0
How does the degree of novelty impacts semi-supervised representation learning for novel class retrieval?—0
Breaking through the learning plateaus of in-context learning in Transformer—0
How Do the Architecture and Optimizer Affect Representation Learning? On the Training Dynamics of Representations in Deep Neural Networks—0
Perceptual Inductive Bias Is What You Need Before Contrastive Learning—0
How do Cross-View and Cross-Modal Alignment Affect Representations in Contrastive Learning?—0
How Diffusion Models Learn to Factorize and Compose—0
PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training—0
3D Shape Classification Using Collaborative Representation based Projections—0
Performance or Trust? Why Not Both. Deep AUC Maximization with Self-Supervised Learning for COVID-19 Chest X-ray Classifications—0
Pre-training Co-evolutionary Protein Representation via A Pairwise Masked Language Model—0
Pre-Training Transformers as Energy-Based Cloze Models—0
RLHF from Heterogeneous Feedback via Personalization and Preference Aggregation—0
How Benign is Benign Overfitting ?—0
Deep Generative Model with Beta Bernoulli Process for Modeling and Learning Confounding Factors—0
PersGNN: Applying Topological Data Analysis and Geometric Deep Learning to Structure-Based Protein Function Prediction—0
How benign is benign overfitting?—0
Persistent Homology and Graphs Representation Learning—0
BioLORD-2023: Semantic Textual Representations Fusing LLM and Clinical Knowledge Graph Insights—0
HoughCL: Finding Better Positive Pairs in Dense Self-supervised Learning—0
Finding Similar Exercises in Retrieval Manner—0
Personalized Federated Learning via Sequential Layer Expansion in Representation Learning—0
HOT: Higher-Order Dynamic Graph Representation Learning with Efficient Transformers—0
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System—0
Personalized Federated Recommendation via Joint Representation Learning, User Clustering, and Model Adaptation—0
Finding the Trigger: Causal Abductive Reasoning on Video Events—0
HOTFormerLoc: Hierarchical Octree Transformer for Versatile Lidar Place Recognition Across Ground and Aerial Views—0
Hospital-Agnostic Image Representation Learning in Digital Pathology—0
A comparison of self-supervised speech representations as input features for unsupervised acoustic word embeddings—0
Personalizing Pre-trained Models—0
Person-Job Fit: Adapting the Right Talent for the Right Job with Joint Representation Learning—0
Horizontal and Vertical Ensemble with Deep Representation for Classification—0
Person search: New paradigm of person re-identification: A survey and outlook of recent works—0
Gaze Prediction as a Function of Eye Movement Type and Individual Differences—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
DeepGate3: Towards Scalable Circuit Representation Learning—0
Hop-Hop Relation-aware Graph Neural Networks—0
PGAHum: Prior-Guided Geometry and Appearance Learning for High-Fidelity Animatable Human Reconstruction—0
Bio-Inspired Representation Learning for Visual Attention Prediction—0
PGX: A Multi-level GNN Explanation Framework Based on Separate Knowledge Distillation Processes—0
PH2ST:ST-Prompt Guided Histological Hypergraph Learning for Spatial Gene Expression Prediction—0
Phased Progressive Learning with Coupling-Regulation-Imbalance Loss for Imbalanced Data Classification—0
Preserve Pre-trained Knowledge: Transfer Learning With Self-Distillation For Action Recognition—0
Relational Graph Neural Network Design via Progressive Neural Architecture Search—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