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 8501–8550 of 10307 papers

TitleStatusHype
Brain MRI Tumor Segmentation with Adversarial Networks—0
A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis—0
Fine-grained Sentiment Classification using BERTCode0
Manufacturing Dispatching using Reinforcement and Transfer Learning—0
Revisiting Classical Bagging with Modern Transfer Learning for On-the-fly Disaster Damage Detector—0
Data-Efficient Goal-Oriented Conversation with Dialogue Knowledge Transfer Networks—0
Harnessing the Power of Infinitely Wide Deep Nets on Small-data TasksCode0
Piracy Resistant Watermarks for Deep Neural NetworksCode0
A Robust Transferable Deep Learning Framework for Cross-sectional Investment Strategy—0
Adversarially Robust Few-Shot Learning: A Meta-Learning ApproachCode0
Application of Low-resource Machine Translation Techniques to Russian-Tatar Language Pair—0
A Survey of Methods to Leverage Monolingual Data in Low-resource Neural Machine Translation—0
Generating Abstractive Summaries with Finetuned Language Models—0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Self-Supervised Representation Learning From Multi-Domain Data—0
Practical Deep Learning for Cloud, Mobile, and EdgeCode0
Multilingual End-to-End Speech Translation—0
Few-Shot Image Recognition With Knowledge TransferCode0
MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding—0
Deep learning for Chemometric and non-translational dataCode0
Attention Bridging Network for Knowledge Transfer—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
Learning Robust Data Representation: A Knowledge Flow Perspective—0
On Generalizing Detection Models for Unconstrained EnvironmentsCode0
Generative One-Shot Face Recognition—0
Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning—0
Reweighted Proximal Pruning for Large-Scale Language Representation—0
Multi-modal Page Stream Segmentation with Convolutional Neural NetworksCode0
Breast Cancer Diagnosis with Transfer Learning and Global Pooling—0
Deep Model Transferability from Attribution MapsCode0
MERL: Multi-Head Reinforcement Learning—0
DARTS: Dialectal Arabic Transcription System—0
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels—0
The Frechet Distance of training and test distribution predicts the generalization gap—0
Knowledge Transfer via Student-Teacher Collaboration—0
Temporal Probabilistic Asymmetric Multi-task Learning—0
Revisiting Gradient Episodic Memory for Continual Learning—0
A Base Model Selection Methodology for Efficient Fine-Tuning—0
A closer look at network resolution for efficient network design—0
Multi-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings—0
Robust Instruction-Following in a Situated Agent via Transfer-Learning from Text—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
DASGrad: Double Adaptive Stochastic Gradient—0
A Copula approach for hyperparameter transfer learning—0
Generalized Domain Adaptation with Covariate and Label Shift CO-ALignment—0
Language-independent Cross-lingual Contextual Representations—0
Transfer Active Learning For Graph Neural Networks—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
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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