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 3051–3100 of 10580 papers

TitleStatusHype
Connecting Joint-Embedding Predictive Architecture with Contrastive Self-supervised Learning—0
Attention-wise masked graph contrastive learning for predicting molecular property—0
Connecting Data to Mechanisms with Meta Structual Causal Model—0
ConKI: Contrastive Knowledge Injection for Multimodal Sentiment Analysis—0
Functional2Structural: Cross-Modality Brain Networks Representation Learning—0
Fundamental Limits and Tradeoffs in Invariant Representation Learning—0
Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis—0
Attention to Detail: Fine-Scale Feature Preservation-Oriented Geometric Pre-training for AI-Driven Surrogate Modeling—0
Confounder Balancing in Adversarial Domain Adaptation for Pre-Trained Large Models Fine-Tuning—0
Attention is All You Need? Good Embeddings with Statistics are enough:Large Scale Audio Understanding without Transformers/ Convolutions/ BERTs/ Mixers/ Attention/ RNNs or ....—0
A Joint Representation Learning and Feature Modeling Approach for One-class Recognition—0
Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks—0
Configurable Spatial-Temporal Hierarchical Analysis for Flexible Video Anomaly Detection—0
A Joint Network Optimization Framework to Predict Clinical Severity from Resting State Functional MRI Data—0
Conditional Synthetic Food Image Generation—0
Attention De-sparsification Matters: Inducing Diversity in Digital Pathology Representation Learning—0
Accelerating Learned Video Compression via Low-Resolution Representation Learning—0
Secure Embedding Aggregation for Federated Representation Learning—0
FUNCK: Information Funnels and Bottlenecks for Invariant Representation Learning—0
Conditional Mutual information-based Contrastive Loss for Financial Time Series Forecasting—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
Conditional Instrumental Variable Regression with Representation Learning for Causal Inference—0
Attention-Based Learning on Molecular Ensembles—0
Random Client Selection on Contrastive Federated Learning for Tabular Data—0
Attention Augmented Convolutional Transformer for Tabular Time-series—0
Denoising-Contrastive Alignment for Continuous Sign Language Recognition—0
Accelerating exploration and representation learning with offline pre-training—0
Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning—0
From superposition to sparse codes: interpretable representations in neural networks—0
Attention Aided CSI Wireless Localization—0
Concept Representation Learning with Contrastive Self-Supervised Learning—0
AIPNet: Generative Adversarial Pre-training of Accent-invariant Networks for End-to-end Speech Recognition—0
Concept-Oriented Deep Learning—0
AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations—0
Concentric Spherical GNN for 3D Representation Learning—0
Adaptive Dual Channel Convolution Hypergraph Representation Learning for Technological Intellectual Property—0
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes—0
Fused Acoustic and Text Encoding for Multimodal Bilingual Pretraining and Speech Translation—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
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
A Triplet-loss Dilated Residual Network for High-Resolution Representation Learning in Image Retrieval—0
Compressed Video Contrastive Learning—0
Learning Repetition-Invariant Representations for Polymer Informatics—0
Comprehensive Multi-Dataset Evaluation of Reading Comprehension—0
Adaptive Discovering and Merging for Incremental Novel Class Discovery—0
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba—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