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 8526–8550 of 10307 papers

TitleStatusHype
Multi-modal Page Stream Segmentation with Convolutional Neural NetworksCode0
Reweighted Proximal Pruning for Large-Scale Language Representation—0
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning—0
MERL: Multi-Head Reinforcement Learning—0
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels—0
Deep Model Transferability from Attribution MapsCode0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—0
DARTS: Dialectal Arabic Transcription System—0
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text—0
Temporal Probabilistic Asymmetric Multi-task Learning—0
Automated identification of neural cells in the multi-photon images using deep-neural networks—0
Adversarial Inductive Transfer Learning with input and output space adaptation—0
A closer look at network resolution for efficient network design—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment—0
A Copula approach for hyperparameter transfer learning—0
Knowledge Transfer via Student-Teacher Collaboration—0
The Frechet Distance of training and test distribution predicts the generalization gap—0
DASGrad: Double Adaptive Stochastic Gradient—0
Revisiting Gradient Episodic Memory for Continual Learning—0
Language-independent Cross-lingual Contextual Representations—0
Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
Weighted Empirical Risk Minimization: Transfer Learning based on Importance Sampling—0
Towards Scalable Imitation Learning for Multi-Agent Systems with Graph Neural Networks—0
Transfer Active Learning For Graph Neural Networks—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