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 4551–4575 of 10307 papers

TitleStatusHype
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
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
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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