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 4351–4375 of 10307 papers

TitleStatusHype
GVdoc: Graph-based Visual Document ClassificationCode0
Collective Knowledge Graph Completion with Mutual Knowledge Distillation—0
Transfer Learning for Personality Perception via Speech Emotion Recognition—0
Representation Transfer Learning via Multiple Pre-trained models for Linear Regression—0
READ: Recurrent Adaptation of Large Transformers—0
Deep Learning-based Bio-Medical Image Segmentation using UNet Architecture and Transfer Learning—0
Few-shot Unified Question Answering: Tuning Models or Prompts?—0
Topic-driven Distant Supervision Framework for Macro-level Discourse Parsing—0
Beyond Shared Vocabulary: Increasing Representational Word Similarities across Languages for Multilingual Machine TranslationCode0
Selective Pre-training for Private Fine-tuningCode0
Deep Transductive Transfer Learning for Automatic Target Recognition—0
Amplitude-Independent Machine Learning for PPG through Visibility Graphs and Transfer Learning—0
Cross-lingual Knowledge Transfer and Iterative Pseudo-labeling for Low-Resource Speech Recognition with Transducers—0
A Two-Step Deep Learning Method for 3DCT-2DUS Kidney Registration During Breathing—0
Feasibility of Transfer Learning: A Mathematical Framework—0
Strategy Extraction in Single-Agent Games—0
A Comprehensive Survey of Sentence Representations: From the BERT Epoch to the ChatGPT Era and Beyond—0
Beyond Labels: Empowering Human Annotators with Natural Language Explanations through a Novel Active-Learning ArchitectureCode0
An Optimized Ensemble Deep Learning Model For Brain Tumor Classification—0
Stock and market index prediction using Informer network—0
Explaining Emergent In-Context Learning as Kernel Regression—0
Sequential Transfer Learning to Decode Heard and Imagined Timbre from fMRI Data—0
Crosslingual Transfer Learning for Low-Resource Languages Based on Multilingual Colexification GraphsCode0
Cross-lingual Transfer Can Worsen Bias in Sentiment Analysis—0
LEAN: Light and Efficient Audio Classification Network—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