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 4001–4050 of 10307 papers

TitleStatusHype
Decomposition-Based Transfer Distance Metric Learning for Image Classification—0
FedLED: Label-Free Equipment Fault Diagnosis with Vertical Federated Transfer Learning—0
A Multi-media Approach to Cross-lingual Entity Knowledge Transfer—0
Decomposed Cross-modal Distillation for RGB-based Temporal Action Detection—0
FedMEKT: Distillation-based Embedding Knowledge Transfer for Multimodal Federated Learning—0
FedMetaMed: Federated Meta-Learning for Personalized Medication in Distributed Healthcare Systems—0
Decomposable Probability-of-Success Metrics in Algorithmic Search—0
FedMobile: Enabling Knowledge Contribution-aware Multi-modal Federated Learning with Incomplete Modalities—0
Automated Segmentation and Analysis of Microscopy Images of Laser Powder Bed Fusion Melt Tracks—0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
Adaptive Transfer Learning for Plant Phenotyping—0
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer—0
Computer-aided Diagnosis of Malaria through Transfer Learning using the ResNet50 Backbone—0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation—0
Gendec: A Machine Learning-based Framework for Gender Detection from Japanese Names—0
General Embedding vs. Task-Specific Embedding: A Comparative Approach to Enhancing NLP Performance—0
FedTune: A Deep Dive into Efficient Federated Fine-Tuning with Pre-trained Transformers—0
FedZKT: Zero-Shot Knowledge Transfer towards Resource-Constrained Federated Learning with Heterogeneous On-Device Models—0
Minimax Excess Risk of First-Order Methods for Statistical Learning with Data-Dependent Oracles—0
FEED: Feature-level Ensemble Effect for knowledge Distillation—0
Generalized and Transferable Patient Language Representation for Phenotyping with Limited Data—0
Few-shot Adaptive Object Detection with Cross-Domain CutMix—0
Generative Adversarial Data Programming—0
Few-Shot Bayesian Optimization with Deep Kernel Surrogates—0
Global Extreme Heat Forecasting Using Neural Weather Models—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Hate Speech Detection and Racial Bias Mitigation in Social Media based on BERT model—0
Few-shot Classification via Ensemble Learning with Multi-Order Statistics—0
FEW SHOT CROP MAPPING USING TRANSFORMERS AND TRANSFER LEARNING WITH SENTINEL-2 TIME SERIES: CASE OF KAIROUAN TUNISIA—0
Few-Shot Cross-Lingual TTS Using Transferable Phoneme Embedding—0
Decoding Working-Memory Load During n-Back Task Performance from High Channel NIRS Data—0
Few-Shot Dialogue Summarization via Skeleton-Assisted Prompt Transfer in Prompt Tuning—0
Few-Shot Domain Adaptation for Grammatical Error Correction via Meta-Learning—0
Few-shot fault diagnosis based on multi-scale graph convolution filtering for industry—0
Automated Scoring of Clinical Expressive Language Evaluation Tasks—0
A Comparative Study on Transfer Learning and Distance Metrics in Semantic Clustering over the COVID-19 Tweets—0
Decision Support System for Detection and Classification of Skin Cancer using CNN—0
Covariate-Elaborated Robust Partial Information Transfer with Conditional Spike-and-Slab Prior—0
Automated Pruning for Deep Neural Network Compression—0
A multilingual training strategy for low resource Text to Speech—0
Few-Shot Learning-Based Human Activity Recognition—0
A Multi-stage Transfer Learning Framework for Diabetic Retinopathy Grading on Small Data—0
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation—0
Concurrent Discrimination and Alignment for Self-Supervised Feature Learning—0
Condensed Sample-Guided Model Inversion for Knowledge Distillation—0
When Few-Shot Learning Meets Video Object Detection—0
Gaze-Net: Appearance-Based Gaze Estimation using Capsule Networks—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
Few-Shot Meta-Denoising—0
Deciphering and Optimizing Multi-Task Learning: a Random Matrix Approach—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