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 6026–6050 of 10307 papers

TitleStatusHype
Self-Supervised Human Activity Recognition with Localized Time-Frequency Contrastive Representation Learning—0
Hate-Speech and Offensive Language Detection in Roman Urdu—0
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model—0
HausaNLP at SemEval-2023 Task 10: Transfer Learning, Synthetic Data and Side-Information for Multi-Level Sexism Classification—0
Have You Poisoned My Data? Defending Neural Networks against Data Poisoning—0
Self-Supervised In-Domain Representation Learning for Remote Sensing Image Scene Classification—0
Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization—0
A scoping review of transfer learning research on medical image analysis using ImageNet—0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling—0
A Scenario-Based Functional Testing Approach to Improving DNN Performance—0
Headless Horseman: Adversarial Attacks on Transfer Learning Models—0
Head-Tail-Aware KL Divergence in Knowledge Distillation for Spiking Neural Networks—0
Headword-Oriented Entity Linking: A Special Entity Linking Task with Dataset and Baseline—0
HeartBEiT: Vision Transformer for Electrocardiogram Data Improves Diagnostic Performance at Low Sample Sizes—0
Rethinking Low-Rank Adaptation in Vision: Exploring Head-Level Responsiveness across Diverse Tasks—0
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer—0
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective?—0
Hello, It's GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems—0
HeNet: A Deep Learning Approach on Intel^ Processor Trace for Effective Exploit Detection—0
H-ensemble: An Information Theoretic Approach to Reliable Few-Shot Multi-Source-Free Transfer—0
A Scalable and Generalized Deep Learning Framework for Anomaly Detection in Surveillance Videos—0
Heterogeneous Continual Learning—0
Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS)—0
Heterogeneous Domain Adaptation for IoT Intrusion Detection: A Geometric Graph Alignment Approach—0
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated 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