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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 751–775 of 10307 papers

TitleStatusHype
VisTabNet: Adapting Vision Transformers for Tabular DataCode0
Cross-Linguistic Examination of Machine Translation Transfer Learning—0
Mouth Articulation-Based Anchoring for Improved Cross-Corpus Speech Emotion Recognition—0
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
EEG-Reptile: An Automatized Reptile-Based Meta-Learning Library for BCIsCode1
Robust Speech and Natural Language Processing Models for Depression Screening—0
SpectralKD: A Unified Framework for Interpreting and Distilling Vision Transformers via Spectral AnalysisCode0
Assessing Pre-trained Models for Transfer Learning through Distribution of Spectral Components—0
Advanced Knowledge Transfer: Refined Feature Distillation for Zero-Shot Quantization in Edge ComputingCode0
Large Language Models for Market Research: A Data-augmentation Approach—0
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge DistillationCode0
Heterogeneous transfer learning for high dimensional regression with feature mismatch—0
VLABench: A Large-Scale Benchmark for Language-Conditioned Robotics Manipulation with Long-Horizon Reasoning Tasks—0
Text-Aware Adapter for Few-Shot Keyword Spotting—0
Bayesian Optimization of Bilevel Problems—0
Improved Cotton Leaf Disease Classification Using Parameter-Efficient Deep Learning Framework—0
CALLIC: Content Adaptive Learning for Lossless Image Compression—0
Feature Based Methods in Domain Adaptation for Object Detection: A Review Paper—0
Speech-Based Depression Prediction Using Encoder-Weight-Only Transfer Learning and a Large Corpus—0
Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-TuningCode0
First-frame Supervised Video Polyp Segmentation via Propagative and Semantic Dual-teacher NetworkCode0
IV-tuning: Parameter-Efficient Transfer Learning for Infrared-Visible TasksCode0
Revisiting MLLMs: An In-Depth Analysis of Image Classification Abilities—0
Learning for Cross-Layer Resource Allocation in MEC-Aided Cell-Free Networks—0
Show:102550
← PrevPage 31 of 413Next →
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