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 1526–1550 of 10307 papers

TitleStatusHype
PromptNER: A Prompting Method for Few-shot Named Entity Recognition via k Nearest Neighbor SearchCode1
Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation ProblemCode1
Pruner: A Speculative Exploration Mechanism to Accelerate Tensor Program TuningCode1
PT-CoDE: Pre-trained Context-Dependent Encoder for Utterance-level Emotion RecognitionCode1
PTGB: Pre-Train Graph Neural Networks for Brain Network AnalysisCode1
Pushing the Limits of Simple Pipelines for Few-Shot Learning: External Data and Fine-Tuning Make a DifferenceCode1
Assessing Large Language Models for Online Extremism Research: Identification, Explanation, and New Knowledge—0
Assessing and Learning Alignment of Unimodal Vision and Language Models—0
A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction—0
A Checkpoint on Multilingual Misogyny Identification—0
Co-Creative Learning via Metropolis-Hastings Interaction between Humans and AI—0
ASR-based Features for Emotion Recognition: A Transfer Learning Approach—0
A Generate-Validate Approach to Answering Questions about Qualitative Relationships—0
CoCo DistillNet: a Cross-layer Correlation Distillation Network for Pathological Gastric Cancer Segmentation—0
Aspect-Based Sentiment Analysis and Singer Name Entity Recognition using Parameter Generation Network Based Transfer Learning—0
A General Regularization Framework for Domain Adaptation—0
Adaptation of Deep Bidirectional Transformers for Afrikaans Language—0
CoCoNet: A Collaborative Convolutional Network—0
Aspect and Opinion Term Extraction for Hotel Reviews using Transfer Learning and Auxiliary Labels—0
A Smartphone-Based Skin Disease Classification Using MobileNet CNN—0
A Smart Healthcare System for Monkeypox Skin Lesion Detection and Tracking—0
2nd Place Solution to ECCV 2020 VIPriors Object Detection Challenge—0
Coarse-To-Fine And Cross-Lingual ASR Transfer—0
A General Multi-Task Learning Framework to Leverage Text Data for Speech to Text Tasks—0
A Situated Dialogue System for Learning Structural Concepts in Blocks World—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