Tiny ImageNet

Tiny ImageNet is a smaller-scale version of the ImageNet dataset, commonly used to benchmark the performance of deep learning models under data-scarcity conditions. Current research focuses on improving model accuracy and robustness on this dataset, exploring techniques like dataset distillation, data augmentation (including image generation and transformations), and novel training methods such as companion learning and sharpness-aware minimization. These efforts aim to bridge the performance gap between convolutional neural networks and vision transformers on limited data, impacting both the development of more efficient models and the advancement of continual and federated learning paradigms.

Papers

June 22, 2023

Squeeze, Recover and Relabel: Dataset Condensation at ImageNet Scale From A New Perspective
Zeyuan Yin, Eric Xing, Zhiqiang Shen
High Performing 1D ConvNet Prompt Recovery Tiny ImageNet Dataset Condensation Squeeze Flow ImageNet Hierarchy

May 25, 2023

Batch Model Consolidation: A Multi-Task Model Consolidation Framework
Iordanis Fostiropoulos, Jiaye Zhu, Laurent Itti
Continual LEArning Mini Batch Multi Task Model Tiny ImageNet Group Loss

May 9, 2023

DietCNN: Multiplication-free Inference for Quantized CNNs
Swarnava Dey, Pallab Dasgupta, Partha P Chakrabarti
Convolutional Neural Network Tiny ImageNet Convolution Technique

April 24, 2023

Neurogenesis Dynamics-inspired Spiking Neural Network Training Acceleration
Shaoyi Huang, Haowen Fang, Kaleel Mahmood, Bowen Lei, Nuo Xu, Bin Lei, Yue Sun, Dongkuan Xu, Wujie Wen, Caiwen Ding
Spiking Neural Network Tiny ImageNet SNN Model Set Prediction Network

April 8, 2023

EMP-SSL: Towards Self-Supervised Learning in One Training Epoch
Shengbang Tong, Yubei Chen, Yi Ma, Yann Lecun
Self Supervised Learning Supervised Learning Self Supervised Method Tiny ImageNet Self Supervised Learning Method Training Epoch

April 3, 2023

Astroformer: More Data Might not be all you need for Classification
Rishit Dagli
Classification Code Tiny ImageNet Hybrid CNN Transformer Galaxy Morphology

March 4, 2023

Hierarchical Training of Deep Neural Networks Using Early Exiting
Yamin Sepehri, Pedram Pad, Ahmet Caner Yüzügüler, Pascal Frossard, L. Andrea Dunbar
Deep Neural Network Edge Cloud Tiny ImageNet Early eXit Hierarchical Training

February 3, 2023

Enhancing Once-For-All: A Study on Parallel Blocks, Skip Connections and Early Exits
Simone Sarti, Eugenio Lomurno, Andrea Falanti, Matteo Matteucci
Neural Network Neural Architecture Search Study Feature Tiny ImageNet Early eXit Honeycomb Block Fit All Approach

October 25, 2022

Explicitly Increasing Input Information Density for Vision Transformers on Small Datasets
Xiangyu Chen, Ying Qin, Wenju Xu, Andrés M. Bur, Cuncong Zhong, Guanghui Wang
Vision Transformer Small Datasets Tiny ImageNet Information Density Focal Transformer

October 23, 2022

Pushing the Efficiency Limit Using Structured Sparse Convolutions
Vinay Kumar Verma, Nikhil Mehta, Shijing Si, Ricardo Henao, Lawrence Carin
Deep Convolutional Neural Network Sparse Subnetworks Tiny ImageNet Convolutional Architecture Performance Limit

October 13, 2022

How to Train Vision Transformer on Small-scale Datasets?
Hanan Gani, Muzammal Naseer, Mohammad Yaqub
Convolutional Neural Network Vision Transformer Tiny ImageNet Small Scale Datasets High Resolution Vision Transformer

October 12, 2022

Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity
Andrew C. Cullen, Paul Montague, Shijie Liu, Sarah M. Erfani, Benjamin I. P. Rubinstein
Native Robustness Tiny ImageNet Manual Effort Certified Robustness Strong Transitivity Relation Asset Price Bubble Sex Trouble Fast Minimum Norm Attack

September 23, 2022

I-SPLIT: Deep Network Interpretability for Split Computing
Federico Cunico, Luigi Capogrosso, Francesco Setti, Damiano Carra, Franco Fummi, Marco Cristani
Deep Neural Network Tiny ImageNet Split and Fit Split Computing Optimal Split

September 6, 2022

Merged-GHCIDR: Geometrical Approach to Reduce Image Data
Devvrat Joshi, Janvi Thakkar, Siddharth Soni, Shril Mody, Rohan Patil, Nipun Batra
Image Data Tiny ImageNet Geometric Approach Projective Clustering

July 27, 2022

Rethinking Efficacy of Softmax for Lightweight Non-Local Neural Networks
Yooshin Cho, Youngsoo Kim, Hanbyel Cho, Jaesung Ahn, Hyeong Gwon Hong, Junmo Kim
Neural Network Attention Map Softmax Function Multi Head Attention Tiny ImageNet Feature Map

July 14, 2022

Universal Adaptive Data Augmentation
Xiaogang Xu, Hengshuang Zhao
Data Augmentation Tiny ImageNet

July 8, 2022

Combining Deep Learning with Good Old-Fashioned Machine Learning
Moshe Sipper
Deep Learning Deep Network Tiny ImageNet Traditional Machine Learning Striking GOld Combining Deep Learning

July 3, 2022

PrUE: Distilling Knowledge from Sparse Teacher Networks
Shaopu Wang, Xiaojun Chen, Mengzhen Kou, Jinqiao Shi
Deep Neural Network Knowledge Distillation Deeper Network Tiny ImageNet Teacher Network Uncertainty Prediction Distilling Knowledge Soft Prediction

May 21, 2022

Vision Transformers in 2022: An Update on Tiny ImageNet
Ethan Huynh
Vision Transformer Tiny ImageNet Image Transformer Rank One Update

May 15, 2022

Proxyless Neural Architecture Adaptation for Supervised Learning and Self-Supervised Learning
Do-Guk Kim, Heung-Chang Lee
Self Supervised Learning Neural Architecture Search Supervised Learning Neural Architecture Tiny ImageNet