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 2201–2225 of 10307 papers

TitleStatusHype
Depression and Anxiety Prediction Using Deep Language Models and Transfer Learning—0
Human-AI Teaming Using Large Language Models: Boosting Brain-Computer Interfacing (BCI) and Brain Research—0
Attention Is All You Need For Mixture-of-Depths Routing—0
Sample Correlation for Fingerprinting Deep Face RecognitionCode0
LEARNER: A Transfer Learning Method for Low-Rank Matrix EstimationCode0
Optical Character Recognition using Convolutional Neural Networks for Ashokan Brahmi Inscriptions—0
On Adversarial Robustness of Language Models in Transfer Learning—0
Enhancing Entertainment Translation for Indian Languages using Adaptive Context, Style and LLMs—0
Enhancing Transfer Learning for Medical Image Classification with SMOTE: A Comparative Study—0
VisTabNet: Adapting Vision Transformers for Tabular DataCode0
Uncertainty Quantified Deep Learning and Regression Analysis Framework for Image Segmentation of Skin Cancer LesionsCode0
LLM-Virus: Evolutionary Jailbreak Attack on Large Language ModelsCode0
Data-driven tool wear prediction in milling, based on a process-integrated single-sensor approach—0
Feature Alignment-Based Knowledge Distillation for Efficient Compression of Large Language Models—0
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition—0
Cross-Linguistic Examination of Machine Translation Transfer Learning—0
Robust Speech and Natural Language Processing Models for Depression Screening—0
Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge ComputingCode0
SpectralKD: A Unified Framework for Interpreting and Distilling Vision Transformers via Spectral AnalysisCode0
Large Language Models for Market Research: A Data-augmentation Approach—0
Assessing Pre-trained Models for Transfer Learning through Distribution of Spectral Components—0
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge DistillationCode0
Text-Aware Adapter for Few-Shot Keyword Spotting—0
VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks—0
Bayesian Optimization of Bilevel Problems—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