# Combining Weakly and Webly Supervised Learning for Classifying Food Images

arXiv: https://arxiv.org/abs/1712.08730 · HTML: https://mlsanity.com/paper/1712.08730/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Dec 2017.

## Built on by (most cited first, 17 of 17)

- [Knowledge distillation: A good teacher is patient and consistent](https://mlsanity.com/paper/2106.05237/) (2021)
- [Revisiting Weight Averaging for Model Merging](https://mlsanity.com/paper/2412.12153/) (2024)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [Experts Weights Averaging: A New General Training Scheme for Vision Transformers](https://mlsanity.com/paper/2308.06093/) (2023)
- [Investigating the Limitation of CLIP Models: The Worst-Performing Categories](https://mlsanity.com/paper/2310.03324/) (2023)
- [All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy Reduction](https://mlsanity.com/paper/2303.09417/) (2023)
- [SplitNet: Learnable Clean-Noisy Label Splitting for Learning with Noisy Labels](https://mlsanity.com/paper/2211.11753/) (2022)
- [GenKL: An Iterative Framework for Resolving Label Ambiguity and Label Non-conformity in Web Images Via a New Generalized KL Divergence](https://mlsanity.com/paper/2307.09810/) (2023)
- [CAPro: Webly Supervised Learning with Cross-Modality Aligned Prototypes](https://mlsanity.com/paper/2310.09761/) (2023)
- [Group Orthogonalization Regularization For Vision Models Adaptation and Robustness](https://mlsanity.com/paper/2306.10001/) (2023)
- [TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration](https://mlsanity.com/paper/2311.15335/) (2023)
- [ForAug: Mitigating Biases in Image Classification via Controlled Image Compositions](https://mlsanity.com/paper/2503.09399/) (2025)
- [Phi: Preference Hijacking in Multi-modal Large Language Models at Inference Time](https://mlsanity.com/paper/2509.12521/) (2025)
- [FoPro: Few-Shot Guided Robust Webly-Supervised Prototypical Learning](https://mlsanity.com/paper/2212.00465/) (2022)
- [Computer Vision Model Compression Techniques for Embedded Systems: A Survey](https://mlsanity.com/paper/2408.08250/) (2024)
- [DCNN: Dual Cross-current Neural Networks Realized Using An Interactive Deep Learning Discriminator for Fine-grained Objects](https://mlsanity.com/paper/2405.04093/) (2024)
- [TextTeacher: What Can Language Teach About Images?](https://mlsanity.com/paper/2605.22098/) (2026)

## Abstract (arXiv)

Food classification from images is a fine-grained classification problem. Manual curation of food images is cost, time and scalability prohibitive. On the other hand, web data is available freely but contains noise. In this paper, we address the problem of classifying food images with minimal data curation. We also tackle a key problems with food images from the web where they often have multiple cooccuring food types but are weakly labeled with a single label. We first demonstrate that by sequentially adding a few manually curated samples to a larger uncurated dataset from two web sources, the top-1 classification accuracy increases from 50.3% to 72.8%. To tackle the issue of weak labels, we augment the deep model with Weakly Supervised learning (WSL) that results in an increase in performance to 76.2%. Finally, we show some qualitative results to provide insights into the performance improvements using the proposed ideas.