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 6201–6250 of 10307 papers

TitleStatusHype
Grounding Hierarchical Reinforcement Learning Models for Knowledge Transfer—0
Group-disentangled Representation Learning with Weakly-Supervised Regularization—0
Grouping-By-ID: Guarding Against Adversarial Domain Shifts—0
Group-Sparse Matrix Factorization for Transfer Learning of Word Embeddings—0
GROWN: GRow Only When Necessary for Continual Learning—0
GRSDet: Learning to Generate Local Reverse Samples for Few-shot Object Detection—0
GruPaTo at SemEval-2020 Task 12: Retraining mBERT on Social Media and Fine-tuned Offensive Language Models—0
GTA: Guided Transfer of Spatial Attention from Object-Centric Representations—0
Guided Generative Adversarial Neural Network for Representation Learning and High Fidelity Audio Generation using Fewer Labelled Audio Data—0
Guided Recommendation for Model Fine-Tuning—0
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning—0
GUST: Quantifying Free-Form Geometric Uncertainty of Metamaterials Using Small Data—0
Habitat classification from satellite observations with sparse annotations—0
HalalNet: A Deep Neural Network that Classifies the Halalness Slaughtered Chicken from their Images—0
Hand Gesture Recognition with Two Stage Approach Using Transfer Learning and Deep Ensemble Learning—0
Handling Variable-Dimensional Time Series with Graph Neural Networks—0
Hand Pose Classification Based on Neural Networks—0
HandsOff: Labeled Dataset Generation With No Additional Human Annotations—0
Hard instance learning for quantum adiabatic prime factorization—0
Harmless Transfer Learning for Item Embeddings—0
Harmonizing knowledge Transfer in Neural Network with Unified Distillation—0
Harnessing Machine Learning for Discerning AI-Generated Synthetic Images—0
Harnessing the Power of Vicinity-Informed Analysis for Classification under Covariate Shift—0
Harnessing Transfer Learning from Swahili: Advancing Solutions for Comorian Dialects—0
Harnessing Transformers: A Leap Forward in Lung Cancer Image Detection—0
Hashtag Healthcare: From Tweets to Mental Health Journals Using Deep Transfer 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
Head2Toe: Utilizing Intermediate Representations for Better OOD Generalization—0
Headache to Overstock? Promoting Long-tail Items through Debiased Product Bundling—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
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
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
Heterogeneous Federated Learning System for Sparse Healthcare Time-Series Prediction—0
Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism—0
Heterogeneous Federated Learning via Personalized Generative Networks—0
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning—0
Heterogeneous Knowledge Transfer in Video Emotion Recognition, Attribution and Summarization—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