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 7051–7100 of 10580 papers

TitleStatusHype
Learning Interpretable Fair Representations—0
Learning Interpretable Concepts: Unifying Causal Representation Learning and Foundation Models—0
Learning Internal Representations (COLT 1995)—0
Learning Internal Representations (PhD Thesis)—0
DLocRL: A Deep Learning Pipeline for Fine-Grained Location Recognition and Linking in Tweets—0
OPTiML: Dense Semantic Invariance Using Optimal Transport for Self-Supervised Medical Image Representation—0
Learning Infomax and Domain-Independent Representations for Causal Effect Inference with Real-World Data—0
Learning in Factored Domains with Information-Constrained Visual Representations—0
Oracle Analysis of Representations for Deep Open Set Detection—0
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension—0
Learning Improved Representations by Transferring Incomplete Evidence Across Heterogeneous Tasks—0
DKT-STDRL: Spatial and Temporal Representation Learning Enhanced Deep Knowledge Tracing for Learning Performance Prediction—0
CEIR: Concept-based Explainable Image Representation Learning—0
Organized Grouped Discrete Representation for Object-Centric Learning—0
Orientation-Disentangled Unsupervised Representation Learning for Computational Pathology—0
Orthogonal Representation Learning for Estimating Causal Quantities—0
Learning Image Representations by Completing Damaged Jigsaw Puzzles—0
OSVNet: Convolutional Siamese Network for Writer Independent Online Signature Verification—0
Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations—0
Learning Hybrid Representations for Automatic 3D Vessel Centerline Extraction—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning—0
Learning High-order Structural and Attribute information by Knowledge Graph Attention Networks for Enhancing Knowledge Graph Embedding—0
Divide and Conquer Self-Supervised Learning for High-Content Imaging—0
Learning Hierarchical Structures On-The-Fly with a Recurrent-Recursive Model for Sequences—0
OVIS: Open-Vocabulary Visual Instance Search via Visual-Semantic Aligned Representation Learning—0
Diversifying Joint Vision-Language Tokenization Learning—0
P^2IR: Universal Deep Node Representation via Partial Permutation Invariant Set Functions—0
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction—0
PaCaNet: A Study on CycleGAN with Transfer Learning for Diversifying Fused Chinese Painting and Calligraphy—0
Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction—0
Learning Hierarchical Graph Representation for Image Manipulation Detection—0
PACER: Preference-conditioned All-terrain Costmap Generation—0
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection—0
PaECTER: Patent-level Representation Learning using Citation-informed Transformers—0
Learning Hierarchical Features with Joint Latent Space Energy-Based Prior—0
Learning Hidden Markov Models with Distributed State Representations for Domain Adaptation—0
DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization—0
Pair DETR: Contrastive Learning Speeds Up DETR Training—0
Pair Distance Distribution: A Model of Semantic Representation—0
Pairwise Representation Learning for Event Coreference—0
Pair-view Unsupervised Graph Representation Learning—0
Pairwise Half-graph Discrimination: A Simple Graph-level Self-supervised Strategy for Pre-training Graph Neural Networks—0
CDPS: Constrained DTW-Preserving Shapelets—0
Revisiting the role of heterophily in graph representation learning: An edge classification perspective—0
Palm up: Playing in the Latent Manifold for Unsupervised Pretraining—0
PAM: Pose Attention Module for Pose-Invariant Face Recognition—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Learning Graph Search Heuristics—0
Learning by Sampling and Compressing: Efficient Graph Representation Learning with Extremely Limited Annotations—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