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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 6476–6500 of 10307 papers

TitleStatusHype
How Does Adversarial Fine-Tuning Benefit BERT?—0
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
SANSformers: Self-Supervised Forecasting in Electronic Health Records with Attention-Free Models—0
AIP: Adversarial Iterative Pruning Based on Knowledge Transfer for Convolutional Neural Networks—0
Deep Learning of Transferable MIMO Channel Modes for 6G V2X Communications—0
Rapidly and accurately estimating brain strain and strain rate across head impact types with transfer learning and data fusion—0
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
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
Targeting Underrepresented Populations in Precision Medicine: A Federated Transfer Learning Approach—0
Canoe : A System for Collaborative Learning for Neural Nets—0
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
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference—0
YANMTT: Yet Another Neural Machine Translation Toolkit—0
TransFER: Learning Relation-aware Facial Expression Representations with Transformers—0
Multi-task learning from fixed-wing UAV images for 2D/3D city modeling—0
CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training—0
A Scaling Law for Synthetic-to-Real Transfer: How Much Is Your Pre-training Effective?Code0
Making Person Search Enjoy the Merits of Person Re-identification—0
Towards Offensive Language Identification for Tamil Code-Mixed YouTube Comments and PostsCode0
EEG-based Classification of Drivers Attention using Convolutional Neural Network—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