SOTAVerified

Transfer Learning

Transfer Learning is a machine learning technique where a model trained on one task is re-purposed and fine-tuned for a related, but different task. The idea behind transfer learning is to leverage the knowledge learned from a pre-trained model to solve a new, but related problem. This can be useful in situations where there is limited data available to train a new model from scratch, or when the new task is similar enough to the original task that the pre-trained model can be adapted to the new problem with only minor modifications.

( Image credit: Subodh Malgonde )

Papers

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Showing 9051–9100 of 10307 papers

TitleStatusHype
HASOCOne@FIRE-HASOC2020: Using BERT and Multilingual BERT models for Hate Speech DetectionCode0
Parameter and Computation Efficient Transfer Learning for Vision-Language Pre-trained ModelsCode0
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image DenoisingCode0
Cascading Adaptors to Leverage English Data to Improve Performance of Question Answering for Low-Resource LanguagesCode0
Meta-Learning Initializations for Image SegmentationCode0
HCR-Net: A deep learning based script independent handwritten character recognition networkCode0
Deep Convolutional Neural Networks for Palm Fruit Maturity ClassificationCode0
Exploiting Graph Structured Cross-Domain Representation for Multi-Domain RecommendationCode0
Representation Learning via Consistent Assignment of Views to ClustersCode0
Explicit Inductive Bias for Transfer Learning with Convolutional NetworksCode0
Meta-Learning on Augmented Gene Expression Profiles for Enhanced Lung Cancer DetectionCode0
Parameter-Efficient and Memory-Efficient Tuning for Vision Transformer: A Disentangled ApproachCode0
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical ImagingCode0
Representation Learning via Consistent Assignment of Views over Random PartitionsCode0
Parameter-Efficient Cross-lingual Transfer of Vision and Language Models via Translation-based AlignmentCode0
Multi-Target Tracking with Transferable Convolutional Neural NetworksCode0
Explicit Alignment Objectives for Multilingual Bidirectional EncodersCode0
Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-TuningCode0
DECAR: Deep Clustering for learning general-purpose Audio RepresentationsCode0
Adapt or Get Left Behind: Domain Adaptation through BERT Language Model Finetuning for Aspect-Target Sentiment ClassificationCode0
Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flowCode0
CARTL: Cooperative Adversarially-Robust Transfer LearningCode0
Deep Categorization with Semi-Supervised Self-Organizing MapsCode0
Meta Transfer Learning for Early Success Prediction in MOOCsCode0
CARL-D: A vision benchmark suite and large scale dataset for vehicle detection and scene segmentationCode0
Cardiac MRI Orientation Recognition and Standardization using Deep Neural NetworksCode0
Deep Asymmetric Multi-task Feature LearningCode0
DeepAffinity: Interpretable Deep Learning of Compound-Protein Affinity through Unified Recurrent and Convolutional Neural NetworksCode0
ASMNet: a Lightweight Deep Neural Network for Face Alignment and Pose EstimationCode0
Explainable Action Advising for Multi-Agent Reinforcement LearningCode0
Decoupling Dynamics and Reward for Transfer LearningCode0
Expert-Free Online Transfer Learning in Multi-Agent Reinforcement LearningCode0
Attention-Guided Lidar Segmentation and Odometry Using Image-to-Point Cloud Saliency TransferCode0
SuPer Deep: A Surgical Perception Framework for Robotic Tissue Manipulation using Deep Learning for Feature ExtractionCode0
Adaptive Transfer Clustering: A Unified FrameworkCode0
Heterogeneous Transfer Learning for Building High-Dimensional Generalized Linear Models with Disparate DatasetsCode0
Decoupled Self Attention for Accurate One Stage Object DetectionCode0
MetaXLR -- Mixed Language Meta Representation Transformation for Low-resource Cross-lingual Learning based on Multi-Armed BanditCode0
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson RegressionCode0
Methods for the frugal labeler: Multi-class semantic segmentation on heterogeneous labelsCode0
Heuristical Comparison of Vision Transformers Against Convolutional Neural Networks for Semantic Segmentation on Remote Sensing ImageryCode0
Attention Based Fully Convolutional Network for Speech Emotion RecognitionCode0
Capturing Pertinent Symbolic Features for Enhanced Content-Based Misinformation DetectionCode0
EXPANSE: A Deep Continual / Progressive Learning System for Deep Transfer LearningCode0
Can We Guide a Multi-Hop Reasoning Language Model to Incrementally Learn at Each Single-Hop?Code0
Parameter-Efficient Transfer Learning for Music Foundation ModelsCode0
Can Unsupervised Knowledge Transfer from Social Discussions Help Argument Mining?Code0
KTNet: Knowledge Transfer for Unpaired 3D Shape CompletionCode0
Can RLHF be More Efficient with Imperfect Reward Models? A Policy Coverage PerspectiveCode0
Adversarial Transfer Learning for Chinese Named Entity Recognition with Self-Attention MechanismCode0
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All datasetsOffice-Home100 sleep nights of 8 caregiversBanglaLekha Isolated DatasetCOCO70KITTI Object Tracking Evaluation 2012Retinal Fundus MultiDisease Image Dataset (RFMiD)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1APCLIPAccuracy84.2—Unverified
2DFA-ENTAccuracy69.2—Unverified
3DFA-SAFNAccuracy69.1—Unverified
4EasyTLAccuracy63.3—Unverified
5MEDAAccuracy60.3—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN10-20% Mask PSNR3.23—Unverified
#ModelMetricClaimedVerifiedStatus
1Chatterjee, Dutta et al.[1]Accuracy96.12—Unverified
#ModelMetricClaimedVerifiedStatus
1Co-TuningAccuracy85.65—Unverified
#ModelMetricClaimedVerifiedStatus
1Physical AccessEER5.74—Unverified
#ModelMetricClaimedVerifiedStatus
1riadd.aucmediAUROC0.95—Unverified