Product Image Recognition with Guidance Learning and Noisy Supervision
arXiv:1907.11384
Abstract
This paper considers recognizing products from daily photos, which is an important problem in real-world applications but also challenging due to background clutters, category diversities, noisy labels, etc. We address this problem by two contributions. First, we introduce a novel large-scale product image dataset, termed as Product-90. Instead of collecting product images by labor-and time-intensive image capturing, we take advantage of the web and download images from the reviews of several e-commerce websites where the images are casually captured by consumers. Labels are assigned automatically by the categories of e-commerce websites. Totally the Product-90 consists of more than 140K images with 90 categories. Due to the fact that consumers may upload unrelated images, it is inevitable that our Product-90 introduces noisy labels. As the second contribution, we develop a simple yet efficient \textit{guidance learning} (GL) method for training convolutional neural networks (CNNs) with noisy supervision. The GL method first trains an initial teacher network with the full noisy dataset, and then trains a target/student network with both large-scale noisy set and small manually-verified clean set in a multi-task manner. Specifically, in the stage of student network training, the large-scale noisy data is supervised by its guidance knowledge which is the combination of its given noisy label and the soften label from the teacher network. We conduct extensive experiments on our Products-90 and public datasets, namely Food101, Food-101N, and Clothing1M. Our guidance learning method achieves performance superior to state-of-the-art methods on these datasets.
arXiv comment10 pages
References in corpus (12)
- Distilling the Knowledge in a Neural Network
- Training Deep Neural Networks on Noisy Labels with Bootstrapping
- MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
- Training Convolutional Networks with Noisy Labels
- Deep Learning is Robust to Massive Label Noise
- WebVision Database: Visual Learning and Understanding from Web Data
- Noise Tolerance under Risk Minimization
- RPC: A Large-Scale Retail Product Checkout Dataset
- Joint Optimization Framework for Learning with Noisy Labels
- The Freiburg Groceries Dataset
- CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
- CurriculumNet: Weakly Supervised Learning from Large-Scale Web Images