The recent success of deep neural networks is powered in part by large-scale well-labeled training data. However, it is a daunting task to laboriously annotate an ImageNet-like dateset. On the contrary, it is fairly convenient, fast, and cheap to collect training images from the Web along with their noisy labels. This signifies the need of alternative approaches to training deep neural networks using such noisy labels. Existing methods tackling this problem either try to identify and correct the wrong labels or reweigh the data terms in the loss function according to the inferred noisy rates. Both strategies inevitably incur errors for some of the data points. In this paper, we contend that it is actually better to ignore the labels of some of the data points than to keep them if the labels are incorrect, especially when the noisy rate is high. After all, the wrong labels could mislead a neural network to a bad local optimum. We suggest a two-stage framework for the learning from noisy labels. In the first stage, we identify a small portion of images from the noisy training set of which the labels are correct with a high probability. The noisy labels of the other images are ignored. In the second stage, we train a deep neural network in a semi-supervised manner. This framework effectively takes advantage of the whole training set and yet only a portion of its labels that are most likely correct. Experiments on three datasets verify the effectiveness of our approach especially when the noisy rate is high.
: Webly-supervised learning methods specify the training data gathered from the web @cite_32 @cite_14 @cite_59 @cite_28 . Usually, the gathered web data are companied with noise and have a very large scale; therefore, it is commonly impossible to know the exact noisy rate of each class. Under such a condition, estimation using a small portion of clean data @cite_49 or noise modeling methods @cite_36 @cite_32 are often applied. While in our approach, we refrain from modeling the label noise and use semi-supervised learning instead to automatically propagate the labels of the mostly correct ones to the remaining training data points.
@cite_14 @cite_28 @cite_36 @cite_32 @cite_49 @cite_59
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Large-scale datasets have driven the rapid development of deep neural networks for visual recognition. However, annotating a massive dataset is expensive and time-consuming. Web images and their labels are, in comparison, much easier to obtain, but direct training on such automatically harvested images can lead to unsatisfactory performance, because the noisy labels of Web images adversely affect the learned recognition models. To address this drawback we propose an end-to-end weakly-supervised deep learning framework which is robust to the label noise in Web images. The proposed framework relies on two unified strategies -- random grouping and attention -- to effectively reduce the negative impact of noisy web image annotations. Specifically, random grouping stacks multiple images into a single training instance and thus increases the labeling accuracy at the instance level. Attention, on the other hand, suppresses the noisy signals from both incorrectly labeled images and less discriminative image regions. By conducting intensive experiments on two challenging datasets, including a newly collected fine-grained dataset with Web images of different car models, the superior performance of the proposed methods over competitive baselines is clearly demonstrated. In this paper, we present a study on learning visual recognition models from large scale noisy web data. We build a new database called WebVision, which contains more than @math million web images crawled from the Internet by using queries generated from the 1,000 semantic concepts of the benchmark ILSVRC 2012 dataset. Meta information along with those web images (e.g., title, description, tags, etc.) are also crawled. A validation set and test set containing human annotated images are also provided to facilitate algorithmic development. Based on our new database, we obtain a few interesting observations: 1) the noisy web images are sufficient for training a good deep CNN model for visual recognition; 2) the model learnt from our WebVision database exhibits comparable or even better generalization ability than the one trained from the ILSVRC 2012 dataset when being transferred to new datasets and tasks; 3) a domain adaptation issue (a.k.a., dataset bias) is observed, which means the dataset can be used as the largest benchmark dataset for visual domain adaptation. Our new WebVision database and relevant studies in this work would benefit the advance of learning state-of-the-art visual models with minimum supervision based on web data. We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being corrupted into another. We further show how one can estimate these probabilities, adapting a recent technique for noise estimation to the multi-class setting, and thus providing an end-to-end framework. Extensive experiments on MNIST, IMDB, CIFAR-10, CIFAR-100 and a large scale dataset of clothing images employing a diversity of architectures --- stacking dense, convolutional, pooling, dropout, batch normalization, word embedding, LSTM and residual layers --- demonstrate the noise robustness of our proposals. Incidentally, we also prove that, when ReLU is the only non-linearity, the loss curvature is immune to class-dependent label noise. Video recognition usually requires a large amount of training samples, which are expensive to be collected. An alternative and cheap solution is to draw from the large-scale images and videos from the Web. With modern search engines, the top ranked images or videos are usually highly correlated to the query, implying the potential to harvest the labeling-free Web images and videos for video recognition. However, there are two key difficulties that prevent us from using the Web data directly. First, they are typically noisy and may be from a completely different domain from that of users’ interest (e.g. cartoons). Second, Web videos are usually untrimmed and very lengthy, where some query-relevant frames are often hidden in between the irrelevant ones. A question thus naturally arises: to what extent can such noisy Web images and videos be utilized for labeling-free video recognition? In this paper, we propose a novel approach to mutually voting for relevant Web images and video frames, where two forces are balanced, i.e. aggressive matching and passive video frame selection. We validate our approach on three large-scale video recognition datasets.