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 5501–5550 of 10580 papers

TitleStatusHype
Conditional Synthetic Food Image Generation—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data—0
Accelerating Learned Video Compression via Low-Resolution Representation Learning—0
Evolving Losses for Unsupervised Video Representation Learning—0
Evolving Losses for Unlabeled Video Representation Learning—0
Evolving Image Compositions for Feature Representation Learning—0
Evolving Dictionary Representation for Few-shot Class-incremental Learning—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—0
Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning—0
Conditional Meta-Learning of Linear Representations—0
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity—0
Attention-based LSTM Network for Cross-Lingual Sentiment Classification—0
Everything Perturbed All at Once: Enabling Differentiable Graph Attacks—0
Everything is Connected: Graph Neural Networks—0
Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning—0
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference—0
EventNeRF: Neural Radiance Fields from a Single Colour Event Camera—0
EventKE: Event-Enhanced Knowledge Graph Embedding—0
Denoising-Contrastive Alignment for Continuous Sign Language Recognition—0
Attention-Based Learning on Molecular Ensembles—0
Attention Augmented Convolutional Transformer for Tabular Time-series—0
Event-Guided Person Re-Identification via Sparse-Dense Complementary Learning—0
EVC: Towards Real-Time Neural Image Compression with Mask Decay—0
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning—0
Evaluation by Association: A Systematic Study of Quantitative Word Association Evaluation—0
Evaluating Unsupervised Representation Learning for Detecting Stances of Fake News—0
Evaluating unsupervised disentangled representation learning for genomic discovery and disease risk prediction—0
Evaluating the Predictive Features of Person-Centric Knowledge Graph Embeddings: Unfolding Ablation Studies—0
Concept Representation Learning with Contrastive Self-Supervised Learning—0
Attention Aided CSI Wireless Localization—0
AIPNet: Generative Adversarial Pre-training of Accent-invariant Networks for End-to-end Speech Recognition—0
Evaluating the Label Efficiency of Contrastive Self-Supervised Learning for Multi-Resolution Satellite Imagery—0
Evaluating Self-Supervised Speech Representations for Indigenous American Languages—0
Concept-Oriented Deep Learning—0
Concentric Spherical GNN for 3D Representation Learning—0
AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations—0
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property—0
Accelerating exploration and representation learning with offline pre-training—0
Random Client Selection on Contrastive Federated Learning for Tabular Data—0
Evaluating Low-Level Speech Features Against Human Perceptual Data—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Attack-Guided Perceptual Data Generation for Real-World Re-Identification—0
Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets—0
Evaluating Contrastive Learning on Wearable Timeseries for Downstream Clinical Outcomes—0
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
AIGenC: An AI generalisation model via creativity—0
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph 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