The state Supreme Court on Thursday upheld part of a lower court's ruling ordering the Department of Correction to release the labels for its supply of midazolam, one of three drugs Arkansas uses in its lethal injection process. The court said the labels must be released, but said a Pulaski County judge must determine what identifying information other than the manufacturer can be withheld.
Multi-label classification (MLC) assigns multiple labels to each sample. Prior studies show that MLC can be transformed to a sequence prediction problem with a recurrent neural network (RNN) decoder to model the label dependency. However, training a RNN decoder requires a predefined order of labels, which is not directly available in the MLC specification. Besides, RNN thus trained tends to overfit the label combinations in the training set and have difficulty generating unseen label sequences. In this paper, we propose a new framework for MLC which does not rely on a predefined label order and thus alleviates exposure bias. The experimental results on three multi-label classification benchmark datasets show that our method outperforms competitive baselines by a large margin. We also find the proposed approach has a higher probability of generating label combinations not seen during training than the baseline models. The result shows that the proposed approach has better generalization capability.
This post overviews the paper Confident Learning: Estimating Uncertainty in Dataset Labels authored by Curtis G. Northcutt, Lu Jiang, and Isaac L. Chuang. If you've ever used datasets like CIFAR, MNIST, ImageNet, or IMDB, you likely assumed the class labels are correct. Why? Principled approaches for characterizing and finding label errors in massive datasets is challenging and solutions are limited. Surprise: there are likely at least 100,000 label issues in ImageNet. In this post, I discuss an emerging, principled framework to identify label errors, characterize label noise, and learn with noisy labels known as confident learning (CL), open-sourced as the cleanlab Python package.
He, Shiyi (Peking University) | Xu, Chang (UBTECH Sydney AI Centre, SIT, FEIT, University of Sydney) | Guo, Tianyu (Peking University) | Xu, Chao (Peking University) | Tao, Dacheng (UBTECH Sydney AI Centre, SIT, FEIT, University of Sydney)
Humans and animals learn much better when the examples are not randomly presented but organized in a meaningful order which illustrates gradually more concepts, and gradually more complex ones. Inspired by this curriculum learning mechanism, we propose a reinforced multi-label image classification approach imitating human behavior to label image from easy to complex. This approach allows a reinforcement learning agent to sequentially predict labels by fully exploiting image feature and previously predicted labels. The agent discovers the optimal policies through maximizing the long-term reward which reflects prediction accuracies. Experimental results on PASCAL VOC2007 and 2012 demonstrate the necessity of reinforcement multi-label learning and the algorithm’s effectiveness in real-world multi-label image classification tasks.
Partial label learning aims to induce a multi-class classifier from training examples where each of them is associated with a set of candidate labels, among which only one label is valid. The common discriminative solution to learn from partial label examples assumes one parametric model for each class label, whose predictions are aggregated to optimize specific objectives such as likelihood or margin over the training examples. Nonetheless, existing discriminative approaches treat the predictions from all parametric models in an equal manner, where the confidence of each candidate label being the ground-truth label is not differentiated. In this paper, a boosting-style partial label learning approach is proposed to enabling confidence-rated discriminative modeling. Specifically, the ground-truth confidence of each candidate label is maintained in each boosting round and utilized to train the base classifier. Extensive experiments on artificial as well as real-world partial label data sets validate the effectiveness of the confidence-rated discriminative modeling.