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 6251–6300 of 10307 papers

TitleStatusHype
Heterogeneous Multi-task Metric Learning across Multiple Domains—0
Heterogeneous Representation Learning: A Review—0
Heterogeneous domain adaptation: An unsupervised approach—0
Heterogeneous transfer learning for high dimensional regression with feature mismatch—0
Heterogeneous Transfer Learning in Ensemble Clustering—0
Heterogenous Multi-Source Data Fusion Through Input Mapping and Latent Variable Gaussian Process—0
Hey AI Can You Grade My Essay?: Automatic Essay Grading—0
HFedCKD: Toward Robust Heterogeneous Federated Learning via Data-free Knowledge Distillation and Two-way Contrast—0
Hierarchical Continual Reinforcement Learning via Large Language Model—0
Hidden Layers in Perceptual Learning—0
Hidden Markov Models and their Application for Predicting Failure Events—0
Hidden Markov tree models for semantic class induction—0
FacLens: Transferable Probe for Foreseeing Non-Factuality in Large Language Models—0
Hidden State Variability of Pretrained Language Models Can Guide Computation Reduction for Transfer Learning—0
Hierarchical Adaptive Expert for Multimodal Sentiment Analysis—0
Hierarchical Cross-Modality Knowledge Transfer with Sinkhorn Attention for CTC-based ASR—0
Hierarchical Deep Learning with Generative Adversarial Network for Automatic Cardiac Diagnosis from ECG Signals—0
Hierarchical Granularity Transfer Learning—0
Hierarchical Neural Network Approaches for Long Document Classification—0
Hierarchical Optimal Transport for Comparing Histopathology Datasets—0
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG—0
Hierarchical Recurrent Aggregative Generation for Few-Shot NLG—0
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision—0
Hierarchical Relation-Guided Type-Sentence Alignment for Long-Tail Relation Extraction with Distant Supervision—0
Hierarchical Side-Tuning for Vision Transformers—0
Hierarchical Transfer Learning for Multi-label Text Classification—0
Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification—0
MERL: Multi-Head Reinforcement Learning—0
Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes—0
High-Fidelity Accelerated MRI Reconstruction by Scan-Specific Fine-Tuning of Physics-Based Neural Networks—0
High-Order Deep Meta-Learning with Category-Theoretic Interpretation—0
High-Resolution Detection of Earth Structural Heterogeneities from Seismic Amplitudes using Convolutional Neural Networks with Attention layers—0
Hi Sigma, do I have the Coronavirus?: Call for a New Artificial Intelligence Approach to Support Health Care Professionals Dealing With The COVID-19 Pandemic—0
Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection—0
HistoTransfer: Understanding Transfer Learning for Histopathology—0
HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems—0
Holistic Multi-Slice Framework for Dynamic Simultaneous Multi-Slice MRI Reconstruction—0
HoloFed: Environment-Adaptive Positioning via Multi-band Reconfigurable Holographic Surfaces and Federated Learning—0
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers—0
Homomorphisms Between Transfer, Multi-Task, and Meta-Learning Systems—0
Homophily and missing links in citation networks—0
Hot PATE: Private Aggregation of Distributions for Diverse Task—0
On Transfer of Adversarial Robustness from Pretraining to Downstream Tasks—0
How Can Cross-lingual Knowledge Contribute Better to Fine-Grained Entity Typing?—0
How Can We Accelerate Progress Towards Human-like Linguistic Generalization?—0
How Different Text-preprocessing Techniques Using The BERT Model Affect The Gender Profiling of Authors—0
How Does Adversarial Fine-Tuning Benefit BERT?—0
How does a Multilingual LM Handle Multiple Languages?—0
How does Architecture Influence the Base Capabilities of Pre-trained Language Models? A Case Study Based on FFN-Wider and MoE Transformers—0
How Does Data Diversity Shape the Weight Landscape of 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