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 5851–5900 of 10307 papers

TitleStatusHype
Contrastive Learning with Temporal Correlated Medical Images: A Case Study using Lung Segmentation in Chest X-RaysCode0
Naturalness Evaluation of Natural Language Generation in Task-oriented Dialogues using BERT—0
CRNNTL: convolutional recurrent neural network and transfer learning for QSAR modelling—0
IndicBART: A Pre-trained Model for Indic Natural Language GenerationCode1
FDA: Feature Decomposition and Aggregation for Robust Airway Segmentation—0
External knowledge transfer deployment inside a simple double agent Viterbi algorithm—0
Deep SIMBAD: Active Landmark-based Self-localization Using Ranking -based Scene Descriptor—0
Training Deep Networks from Zero to Hero: avoiding pitfalls and going beyondCode0
GPT-3 Models are Poor Few-Shot Learners in the Biomedical DomainCode0
Less is More: Lighter and Faster Deep Neural Architecture for Tomato Leaf Disease ClassificationCode1
Robust Importance Sampling for Error Estimation in the Context of Optimal Bayesian Transfer Learning—0
FBDNN: Filter Banks and Deep Neural Networks for Portable and Fast Brain-Computer InterfacesCode0
Automatic Online Multi-Source Domain AdaptationCode0
Dual Transfer Learning for Event-based End-task Prediction via Pluggable Event to Image TranslationCode1
A Comprehensive Approach for UAV Small Object Detection with Simulation-based Transfer Learning and Adaptive FusionCode1
Robust fine-tuning of zero-shot modelsCode1
A Bayesian Approach to (Online) Transfer Learning: Theory and Algorithms—0
CAM-loss: Towards Learning Spatially Discriminative Feature Representations—0
roadscene2vec: A Tool for Extracting and Embedding Road Scene-GraphsCode1
Coarse-To-Fine And Cross-Lingual ASR Transfer—0
Transfer of Pretrained Model Weights Substantially Improves Semi-Supervised Image ClassificationCode0
Using Transfer Learning to Automatically Mark L2 Writing Texts—0
Transfer Learning for Czech Historical Named Entity Recognition—0
InFoBERT: Zero-Shot Approach to Natural Language Understanding Using Contextualized Word Embedding—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
Cross-lingual Fine-tuning for Abstractive Arabic Text Summarization—0
Adapted End-to-End Coreference Resolution System for Anaphoric Identities in Dialogues—0
OptAGAN: Entropy-based finetuning on text VAE-GANCode0
AraT5: Text-to-Text Transformers for Arabic Language GenerationCode1
Sense representations for Portuguese: experiments with sense embeddings and deep neural language models—0
LightNER: A Lightweight Tuning Paradigm for Low-resource NER via Pluggable PromptingCode0
Using convolutional neural networks for the classification of breast cancer imagesCode0
Task-Oriented Dialogue System as Natural Language GenerationCode1
SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models—0
Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications—0
How Does Adversarial Fine-Tuning Benefit BERT?—0
AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks—0
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion—0
Knowledge Base Completion Meets Transfer LearningCode1
Transfer Learning Based Co-surrogate Assisted Evolutionary Bi-objective Optimization for Objectives with Non-uniform Evaluation Times—0
Transfer Learning for Multi-lingual Tasks -- a Survey—0
Prototype-Guided Memory Replay for Continual Learning—0
AP-10K: A Benchmark for Animal Pose Estimation in the WildCode1
Canoe : A System for Collaborative Learning for Neural Nets—0
Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach—0
A Framework for Supervised Heterogeneous Transfer Learning using Dynamic Distribution Adaptation and Manifold RegularizationCode0
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation—0
Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set FrameworkCode1
Segmentation of Shoulder Muscle MRI Using a New Region and Edge based Deep Auto-Encoder—0
Geometry Based Machining Feature Retrieval with Inductive Transfer Learning—0
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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