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 4451–4475 of 10307 papers

TitleStatusHype
Few-shot Classification via Ensemble Learning with Multi-Order Statistics—0
A Transfer Learning Approach to Minimize Reinforcement Learning Risks in Energy Optimization for Smart Buildings—0
Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge—0
Limits of Model Selection under Transfer Learning—0
Synergy of Machine and Deep Learning Models for Multi-Painter RecognitionCode0
HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification—0
NLNDE at SemEval-2023 Task 12: Adaptive Pretraining and Source Language Selection for Low-Resource Multilingual Sentiment Analysis—0
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential RecommendationCode0
Towards Better Domain Adaptation for Self-supervised Models: A Case Study of Child ASRCode0
Maximizing Model Generalization for Machine Condition Monitoring with Self-Supervised Learning and Federated Learning—0
BactInt: A domain driven transfer learning approach and a corpus for extracting inter-bacterial interactions from biomedical text—0
Deep Transfer Learning for Automatic Speech Recognition: Towards Better Generalization—0
Exploiting CNNs for Semantic Segmentation with Pascal VOC—0
Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption—0
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and TechniquesCode0
MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis—0
Injecting structural hints: Using language models to study inductive biases in language learningCode0
GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning—0
Towards Compute-Optimal Transfer Learning—0
Towards Addressing Training Data Scarcity Challenge in Emerging Radio Access Networks: A Survey and Framework—0
Distilling from Similar Tasks for Transfer Learning on a BudgetCode0
Robust Tickets Can Transfer Better: Drawing More Transferable Subnetworks in Transfer Learning—0
How good are variational autoencoders at transfer learning?Code0
Hybrid quantum physics-informed neural networks for simulating computational fluid dynamics in complex shapes—0
KitchenScale: Learning to predict ingredient quantities from recipe contextsCode0
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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