# Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification

arXiv: https://arxiv.org/abs/2411.12151 · HTML: https://mlsanity.com/paper/2411.12151/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 19 Nov 2024.

## Builds on (most cited first, 21 of 21)

- [Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Mining](https://mlsanity.com/paper/2409.14327/) (2024)
- [Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networks](https://mlsanity.com/paper/2410.17617/) (2024)
- [Dynamic Fraud Detection: Integrating Reinforcement Learning into Graph Neural Networks](https://mlsanity.com/paper/2409.09892/) (2024)
- [Research on Adverse Drug Reaction Prediction Model Combining Knowledge Graph Embedding and Deep Learning](https://mlsanity.com/paper/2407.16715/) (2024)
- [Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits](https://mlsanity.com/paper/2408.07084/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessment](https://mlsanity.com/paper/2409.18544/) (2024)
- [Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithm](https://mlsanity.com/paper/2410.12259/) (2024)
- [Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithm](https://mlsanity.com/paper/2410.17586/) (2024)
- [Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentation](https://mlsanity.com/paper/2410.13099/) (2024)
- [Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessment](https://mlsanity.com/paper/2409.17909/) (2024)
- [Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis](https://mlsanity.com/paper/2410.04283/) (2024)
- [Balancing Innovation and Privacy: Data Security Strategies in Natural Language Processing Applications](https://mlsanity.com/paper/2410.08553/) (2024)
- [Deep Learning for Medical Text Processing: BERT Model Fine-Tuning and Comparative Study](https://mlsanity.com/paper/2410.20792/) (2024)
- [Convolutional Neural Networks for Predictive Modeling of Lung Disease](https://mlsanity.com/paper/2408.12605/) (2024)
- [Automated Genre-Aware Article Scoring and Feedback Using Large Language Models](https://mlsanity.com/paper/2410.14165/) (2024)
- [Deep Learning with HM-VGG: AI Strategies for Multi-modal Image Analysis](https://mlsanity.com/paper/2410.24046/) (2024)
- [Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentation](https://mlsanity.com/paper/2409.13868/) (2024)
- [Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Management](https://mlsanity.com/paper/2410.19211/) (2024)
- [Axial Attention Transformer Networks: A New Frontier in Breast Cancer Detection](https://mlsanity.com/paper/2409.12347/) (2024)
- [Optimizing Retrieval-Augmented Generation with Elasticsearch for Enhanced Question-Answering Systems](https://mlsanity.com/paper/2410.14167/) (2024)

## Built on by (most cited first, 11 of 11)

- [Adaptive Optimization for Enhanced Efficiency in Large-Scale Language Model Training](https://mlsanity.com/paper/2412.04718/) (2024)
- [An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction](https://mlsanity.com/paper/2412.02211/) (2024)
- [Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Data](https://mlsanity.com/paper/2411.18622/) (2024)
- [Accurate Medical Named Entity Recognition Through Specialized NLP Models](https://mlsanity.com/paper/2412.08255/) (2024)
- [Advanced Risk Prediction and Stability Assessment of Banks Using Time Series Transformer Models](https://mlsanity.com/paper/2412.03606/) (2024)
- [Dynamic Scheduling Strategies for Resource Optimization in Computing Environments](https://mlsanity.com/paper/2412.17301/) (2024)
- [Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothing](https://mlsanity.com/paper/2412.03097/) (2024)
- [Optimizing Multi-Task Learning for Enhanced Performance in Large Language Models](https://mlsanity.com/paper/2412.06249/) (2024)
- [Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt](https://mlsanity.com/paper/2412.17314/) (2024)
- [Deep Learning in Image Classification: Evaluating VGG19's Performance on Complex Visual Data](https://mlsanity.com/paper/2412.20345/) (2024)
- [Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders](https://mlsanity.com/paper/2412.14521/) (2024)

## Abstract (arXiv)

This study aims to optimize the few-shot image classification task and improve the model's feature extraction and classification performance by combining self-supervised learning with the deep network model ResNet-101. During the training process, we first pre-train the model with self-supervision to enable it to learn common feature expressions on a large amount of unlabeled data; then fine-tune it on the few-shot dataset Mini-ImageNet to improve the model's accuracy and generalization ability under limited data. The experimental results show that compared with traditional convolutional neural networks, ResNet-50, DenseNet, and other models, our method has achieved excellent performance of about 95.12% in classification accuracy (ACC) and F1 score, verifying the effectiveness of self-supervised learning in few-shot classification. This method provides an efficient and reliable solution for the field of few-shot image classification.