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 901–925 of 10307 papers

TitleStatusHype
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
Towards Robust Cross-Domain Recommendation with Joint Identifiability of User Preference—0
Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding—0
Glo-In-One-v2: Holistic Identification of Glomerular Cells, Tissues, and Lesions in Human and Mouse HistopathologyCode0
SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction—0
Ensemble Learning via Knowledge Transfer for CTR PredictionCode0
Towards Foundation Models for Critical Care Time Series—0
Deep Learning for Motion Classification in Ankle Exoskeletons Using Surface EMG and IMU Signals—0
BayLing 2: A Multilingual Large Language Model with Efficient Language AlignmentCode3
Deep Learning for automated multi-scale functional field boundaries extraction using multi-date Sentinel-2 and PlanetScope imagery: Case Study of Netherlands and Pakistan—0
Revelio: Interpreting and leveraging semantic information in diffusion modelsCode1
Partial Knowledge Distillation for Alleviating the Inherent Inter-Class Discrepancy in Federated Learning—0
Knowledge Transfer Across Modalities with Natural Language Supervision—0
MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating TrainingCode1
Trans-Glasso: A Transfer Learning Approach to Precision Matrix Estimation—0
LAGUNA: LAnguage Guided UNsupervised Adaptation with structured spaces—0
Multi-modal Representation Learning Enables Accurate Protein Function Prediction in Low-Data SettingCode0
Personalization of Wearable Sensor-Based Joint Kinematic Estimation Using Computer Vision for Hip Exoskeleton Applications—0
Event USKT : U-State Space Model in Knowledge Transfer for Event Cameras—0
Towards Speaker Identification with Minimal Dataset and Constrained Resources using 1D-Convolution Neural NetworkCode0
Self-Supervised Learning for Ordered Three-Dimensional Structures—0
Implementation of Real-Time Lane Detection on Autonomous Mobile Robot—0
CODE-CL: Conceptor-Based Gradient Projection for Deep Continual LearningCode1
Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation—0
SegBook: A Simple Baseline and Cookbook for Volumetric Medical Image Segmentation—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