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 5651–5700 of 10580 papers

TitleStatusHype
Deep Variational Luenberger-type Observer for Stochastic Video Prediction—0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Learning to schedule job-shop problems: Representation and policy learning using graph neural network and reinforcement learning—0
Learning to See in the Dark with Events—0
Improving VAE-based Representation Learning—0
Improving Unsupervised Subword Modeling via Disentangled Speech Representation Learning and Transformation—0
Measuring and Characterizing Generalization in Deep Reinforcement Learning—0
Measuring the Interpretability of Unsupervised Representations via Quantized Reversed Probing—0
Brain-Driven Representation Learning Based on Diffusion Model—0
Learning unbiased features—0
BrainECHO: Semantic Brain Signal Decoding through Vector-Quantized Spectrogram Reconstruction for Whisper-Enhanced Text Generation—0
Learning Universal Multi-level Market Irrationality Factors to Improve Stock Return Forecasting—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Deep Unsupervised Common Representation Learning for LiDAR and Camera Data using Double Siamese Networks—0
Empowering General-purpose User Representation with Full-life Cycle Behavior Modeling—0
Learning Unseen Emotions from Gestures via Semantically-Conditioned Zero-Shot Perception with Adversarial Autoencoders—0
Learning Unsupervised Semantic Document Representation for Fine-grained Aspect-based Sentiment Analysis—0
Dropping Convexity for More Efficient and Scalable Online Multiview Learning—0
Improving the Unsupervised Disentangled Representation Learning with VAE Ensemble—0
Learning Versatile 3D Shape Generation with Improved AR Models—0
Improving the Robustness of DistilHuBERT to Unseen Noisy Conditions via Data Augmentation, Curriculum Learning, and Multi-Task Enhancement—0
Deep unsupervised anomaly detection—0
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling—0
Improving the Modality Representation with Multi-View Contrastive Learning for Multimodal Sentiment Analysis—0
Learning Video Representations of Human Motion From Synthetic Data—0
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization—0
Deep Unsupervised Active Learning on Learnable Graphs—0
Learning Visual Composition through Improved Semantic Guidance—0
Improving Tail-Class Representation with Centroid Contrastive Learning—0
Improving Subgraph Representation Learning via Multi-View Augmentation—0
Deep tree-ensembles for multi-output prediction—0
Learning Visual Representation from Human Interactions—0
Improving Speech Representation Learning via Speech-level and Phoneme-level Masking Approach—0
Improving Semantic Correspondence with Viewpoint-Guided Spherical Maps—0
Learning Weighted Representations for Generalization Across Designs—0
Learning What to Share: Leaky Multi-Task Network for Text Classification—0
DeepTrax: Embedding Graphs of Financial Transactions—0
Deep Trans-layer Unsupervised Networks for Representation Learning—0
Learning with Capsules: A Survey—0
Learning with Memory Embeddings—0
Improving self-supervised representation learning via sequential adversarial masking—0
A Noise-Robust Self-supervised Pre-training Model Based Speech Representation Learning for Automatic Speech Recognition—0
Fuzzy Tiling Activations: A Simple Approach to Learning Sparse Representations Online—0
Learning Word Representations by Jointly Modeling Syntagmatic and Paradigmatic Relations—0
MDL-CW: A Multimodal Deep Learning Framework With Cross Weights—0
MD-Manifold: A Medical-Distance-Based Representation Learning Approach for Medical Concept and Patient Representation—0
Measure Inducing Classification and Regression Trees for Functional Data—0
Improving Robustness and Generality of NLP Models Using Disentangled Representations—0
Improving Representation Learning of Complex Critical Care Data with ICU-BERT—0
Deep Temporal Contrastive Clustering—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