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 776–800 of 10307 papers

TitleStatusHype
The Master Key Filters Hypothesis: Deep Filters Are General—0
Monkey Transfer Learning Can Improve Human Pose Estimation—0
Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge TransferCode0
Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge Transfer Network—0
SeagrassFinder: Deep Learning for Eelgrass Detection and Coverage Estimation in the Wild—0
The First Multilingual Model For The Detection of Suicide Texts—0
Self-Evolution Knowledge Distillation for LLM-based Machine Translation—0
A Multi-Fidelity Graph U-Net Model for Accelerated Physics Simulations—0
Color Enhancement for V-PCC Compressed Point Cloud via 2D Attribute Map Optimization—0
RefHCM: A Unified Model for Referring Perceptions in Human-Centric ScenariosCode0
Knowledge Distillation in RNN-Attention Models for Early Prediction of Student PerformanceCode0
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object DetectionCode0
Enhancing Knowledge Distillation for LLMs with Response-Priming PromptingCode0
Bridging the User-side Knowledge Gap in Knowledge-aware Recommendations with Large Language ModelsCode1
Language verY Rare for All—0
Trustworthy Transfer Learning: A Survey—0
FlexPose: Pose Distribution Adaptation with Limited Guidance—0
On Explaining Knowledge Distillation: Measuring and Visualising the Knowledge Transfer Process—0
Understanding and Analyzing Model Robustness and Knowledge-Transfer in Multilingual Neural Machine Translation using TX-Ray—0
In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning—0
Extending LLMs to New Languages: A Case Study of Llama and Persian AdaptationCode0
Deep Speech Synthesis from Multimodal Articulatory Representations—0
Multi-Task Reinforcement Learning for Quadrotors—0
A3E: Aligned and Augmented Adversarial Ensemble for Accurate, Robust and Privacy-Preserving EEG Decoding—0
CiTrus: Squeezing Extra Performance out of Low-data Bio-signal Transfer 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