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 10051–10100 of 10307 papers

TitleStatusHype
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based Human Activity Recognition—0
FedPOIRec: Privacy Preserving Federated POI Recommendation with Social Influence—0
FedProK: Trustworthy Federated Class-Incremental Learning via Prototypical Feature Knowledge Transfer—0
FedSKD: Aggregation-free Model-heterogeneous Federated Learning using Multi-dimensional Similarity Knowledge Distillation—0
FedTAD: Topology-aware Data-free Knowledge Distillation for Subgraph Federated Learning—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
FEED: Feature-level Ensemble Effect for knowledge Distillation—0
Few-shot Adaptive Object Detection with Cross-Domain CutMix—0
Few-Shot Bayesian Optimization with Deep Kernel Surrogates—0
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs—0
Few-Shot Classification of Autism Spectrum Disorder using Site-Agnostic Meta-Learning and Brain MRI—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
Few-Shot Dialogue Generation Without Annotated Data: A Transfer Learning Approach—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
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
When Few-Shot Learning Meets Video Object Detection—0
Few-Shot Learning with Intra-Class Knowledge Transfer—0
Few-Shot Load Forecasting Under Data Scarcity in Smart Grids: A Meta-Learning Approach—0
Few-Shot Meta-Denoising—0
Few-shot Multimodal Multitask Multilingual Learning—0
Few-Shot Nested Named Entity Recognition—0
Few-Shot Object Detection via Knowledge Transfer—0
Few-Shot Object Detection with Sparse Context Transformers—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Few-shot Transfer Learning for Holographic Image Reconstruction using a Recurrent Neural Network—0
Few-Shot Transfer Learning for Individualized Braking Intent Detection on Neuromorphic Hardware—0
Few-shot Unified Question Answering: Tuning Models or Prompts?—0
Few-Shot Visual Question Generation: A Novel Task and Benchmark Datasets—0
FEWS: Large-Scale, Low-Shot Word Sense Disambiguation with the Dictionary—0
FGLP: A Federated Fine-Grained Location Prediction System for Mobile Users—0
FiGKD: Fine-Grained Knowledge Distillation via High-Frequency Detail Transfer—0
Figure Eight at SemEval-2019 Task 3: Ensemble of Transfer Learning Methods for Contextual Emotion Detection—0
Filtered Inner Product Projection for Crosslingual Embedding Alignment—0
Filtered Manifold Alignment—0
Filtering DDoS Attacks from Unlabeled Network Traffic Data Using Online Deep Learning—0
Financial Aspect-Based Sentiment Analysis using Deep Representations—0
Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering—0
Finding Quantum Critical Points with Neural-Network Quantum States—0
Findings of the Covid-19 MLIA Machine Translation Task—0
Finding the Most Transferable Tasks for Brain Image Segmentation—0
Fine-grained domain classification using Transformers—0
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems—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