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 4451–4500 of 10307 papers

TitleStatusHype
Structural Similarity for Improved Transfer in Reinforcement Learning—0
A Proper Orthogonal Decomposition approach for parameters reduction of Single Shot Detector networks—0
Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox OptimizationCode3
Applied Computer Vision on 2-Dimensional Lung X-Ray Images for Assisted Medical Diagnosis of Pneumonia—0
Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors—0
AI Approaches in Processing and Using Data in Personalized Medicine—0
AMF: Adaptable Weighting Fusion with Multiple Fine-tuning for Image Classification—0
Active Learning of Ordinal Embeddings: A User Study on Football Data—0
Learning structures of the French clinical language:development and validation of word embedding models using 21 million clinical reports from electronic health records—0
Fine-Tuning BERT for Automatic ADME Semantic Labeling in FDA Drug Labeling to Enhance Product-Specific Guidance Assessment—0
SecretGen: Privacy Recovery on Pre-Trained Models via Distribution DiscriminationCode0
W2N:Switching From Weak Supervision to Noisy Supervision for Object DetectionCode1
From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach—0
ArmanEmo: A Persian Dataset for Text-based Emotion DetectionCode0
Self-supervised contrastive learning of echocardiogram videos enables label-efficient cardiac disease diagnosisCode1
Online Knowledge Distillation via Mutual Contrastive Learning for Visual RecognitionCode1
AdaptCL: Adaptive Continual Learning for Tackling Heterogeneity in Sequential DatasetsCode0
Hyper-Representations for Pre-Training and Transfer LearningCode1
Multi-Level Fine-Tuning, Data Augmentation, and Few-Shot Learning for Specialized Cyber Threat Intelligence—0
Deep transfer learning method based on automatic domain alignment and moment matchingCode0
Completing Single-Cell DNA Methylome Profiles via Transfer Learning Together With KL-DivergenceCode0
A Transferable Recommender Approach for Selecting the Best Density Functional Approximations in Chemical Discovery—0
Federated Semi-Supervised Domain Adaptation via Knowledge Transfer—0
Sequence Models for Drone vs Bird Classification—0
Unsupervised pre-training of graph transformers on patient population graphsCode1
What Do We Maximize in Self-Supervised Learning?—0
Transfer Learning of wav2vec 2.0 for Automatic Lyric TranscriptionCode1
GenHPF: General Healthcare Predictive Framework with Multi-task Multi-source LearningCode1
A Novel Neural Network Training Method for Autonomous Driving Using Semi-Pseudo-Labels and 3D Data Augmentations—0
Assaying Out-Of-Distribution Generalization in Transfer LearningCode0
Heterogeneous Treatment Effect with Trained Kernels of the Nadaraya-Watson RegressionCode0
Online Dynamics Learning for Predictive Control with an Application to Aerial RobotsCode1
Similarity of Pre-trained and Fine-tuned Representations—0
Revealing Secrets From Pre-trained Models—0
Simplified Transfer Learning for Chest Radiography Models Using Less DataCode1
Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent spaceCode0
Tip-Adapter: Training-free Adaption of CLIP for Few-shot ClassificationCode2
On the Usability of Transformers-based models for a French Question-Answering task—0
On the cross-lingual transferability of multilingual prototypical models across NLU tasks—0
Self-Supervised Interactive Object Segmentation Through a Singulation-and-Grasping Approach—0
Multilingual Transformer Encoders: a Word-Level Task-Agnostic EvaluationCode0
Supervised Contrastive ResNet and Transfer Learning for the In-vehicle Intrusion Detection System—0
CTL-MTNet: A Novel CapsNet and Transfer Learning-Based Mixed Task Net for the Single-Corpus and Cross-Corpus Speech Emotion RecognitionCode0
An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesionsCode0
TSPipe: Learn from Teacher Faster with PipelinesCode0
Learning with Recoverable ForgettingCode1
Multi-Task and Transfer Learning for Federated Learning Applications—0
SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite ImageryCode2
Coupling Adversarial Learning with Selective Voting Strategy for Distribution Alignment in Partial Domain Adaptation—0
JPerceiver: Joint Perception Network for Depth, Pose and Layout Estimation in Driving ScenesCode1
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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