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 8351–8400 of 10580 papers

TitleStatusHype
Invariant Causal Representation Learning for Out-of-Distribution Generalization—0
Invariant Causal Representation Learning—0
DHOG: Deep Hierarchical Object Grouping—0
Invariant and consistent: Unsupervised representation learning for few-shot visual recognition—0
Invariance & Causal Representation Learning: Prospects and Limitations—0
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective—0
BYOLMed3D: Self-Supervised Representation Learning of Medical Videos using Gradient Accumulation Assisted 3D BYOL Framework—0
Introducing Diminutive Causal Structure into Graph Representation Learning—0
Intrinsic Geometric Information Transfer Learning on Multiple Graph-Structured Datasets—0
Intrinsic Anomaly Detection for Multi-Variate Time Series—0
Intrinsic and Extrinsic Evaluation of Spatiotemporal Text Representations in Twitter Streams—0
Intrinsically Motivated Self-supervised Learning in Reinforcement Learning—0
INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding—0
Bag of Image Patch Embedding Behind the Success of Self-Supervised Learning—0
DGA-Net Dynamic Gaussian Attention Network for Sentence Semantic Matching—0
An Overview on Data Representation Learning: From Traditional Feature Learning to Recent Deep Learning—0
Adversarially learning disentangled speech representations for robust multi-factor voice conversion—0
Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness—0
Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play—0
BUTTER: A Representation Learning Framework for Bi-directional Music-Sentence Retrieval and Generation—0
Interventional Imbalanced Multi-Modal Representation Learning via β-Generalization Front-Door Criterion—0
Interventional Contrastive Learning with Meta Semantic Regularizer—0
BURT: BERT-inspired Universal Representation from Learning Meaningful Segment—0
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification—0
Interrogating Paradigms in Self-supervised Graph Representation Learning—0
Interpreting What Typical Fault Signals Look Like via Prototype-matching—0
Devil's Hand: Data Poisoning Attacks to Locally Private Graph Learning Protocols—0
Device Directedness with Contextual Cues for Spoken Dialog Systems—0
Learning Disentangled Representations for Time Series—0
Development of a robust cascaded architecture for intelligent robot grasping using limited labelled data—0
Interpretable time series neural representation for classification purposes—0
Developing a PET/CT Foundation Model for Cross-Modal Anatomical and Functional Imaging—0
Building Shortcuts between Distant Nodes with Biaffine Mapping for Graph Convolutional Networks—0
A Novel Two-Step Method for Cross Language Representation Learning—0
Interpretable Sentence Representation with Variational Autoencoders and Attention—0
Interpretable representation learning of quantum data enabled by probabilistic variational autoencoders—0
Detector Discovery in the Wild: Joint Multiple Instance and Representation Learning—0
Interpretable Representation Learning of Cardiac MRI via Attribute Regularization—0
Interpretable Representation Learning from Videos using Nonlinear Priors—0
Detection of Unknown Anomalies in Streaming Videos with Generative Energy-based Boltzmann Models—0
Interpretable Representation Learning for Additive Rule Ensembles—0
Interpretable Node Representation with Attribute Decoding—0
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach—0
Interpretable Load Forecasting via Representation Learning of Geo-distributed Meteorological Factors—0
Interpretable Learning Dynamics in Unsupervised Reinforcement Learning—0
Detection and Description of Change in Visual Streams—0
Interpretable Foreground Object Search As Knowledge Distillation—0
Detecting of a Patient's Condition From Clinical Narratives Using Natural Language Representation—0
Budgeted Embedding Table For Recommender Systems—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
6CiteBERTAvg.58.8—Unverified
7BioBERTAvg.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