Crowdsourcing allows to instantly recruit workers on the web to annotate image, web page, or document databases. However, worker unreliability prevents taking a workers responses at face value. Thus, responses from multiple workers are typically aggregated to more reliably infer ground-truth answers. We study two approaches for crowd aggregation on multicategory answer spaces stochastic modeling based and deterministic objective function based. Our stochastic model for answer generation plausibly captures the interplay between worker skills, intentions, and task difficulties and allows us to model a broad range of worker types. Our deterministic objective based approach does not assume a model for worker response generation. Instead, it aims to maximize the average aggregate confidence of weighted plurality crowd decision making. In both approaches, we explicitly model the skill and intention of individual workers, which is exploited for improved crowd aggregation. Our methods are applicable in both unsupervised and semisupervised settings, and also when the batch of tasks is heterogeneous. As observed experimentally, the proposed methods can defeat tyranny of the masses, they are especially advantageous when there is a minority of skilled workers amongst a large crowd of unskilled and malicious workers.
Adversarial workers in the binary case were accounted for in @cite_11 and @cite_0 . In this work, we characterized adversarial behavior for a more generalized (multicategory) setting and proposed several realistic adversarial models. We also showed how we can retain the interpretation of negative weights as representing adversaries in generality from the binary @cite_0 to the multicategory case. Moreover, we showed that our approach exploits responses from (simple) adversaries to actually improve the overall performance. @cite_0 and @cite_21 consider other statistical methods, such as correlation-based rules and low rank approximation of matrices. These methods have been studied for binary classification tasks. Our objective-based approach generalizes the weighted majority theme of these papers to a multicategory case, incorporating honest workers, adversaries, and spammers. We note that recently, @cite_4 have tried to extend the low rank approximation approach to the case when the tasks do not have a ground truth answer and the answers (from a categorical space) can be subjective. In this case, schools of thought" are discovered via clustering and the average size of clusters for each task is representative of its ease (clarity).
@cite_0 @cite_21 @cite_4 @cite_11
2616969219 2900946294 2773012335 2768762802
Generative adversarial networks (GANs) have great successes on synthesizing data. However, the existing GANs restrict the discriminator to be a binary classifier, and thus limit their learning capacity for tasks that need to synthesize output with rich structures such as natural language descriptions. In this paper, we propose a novel generative adversarial network, RankGAN, for generating high-quality language descriptions. Rather than training the discriminator to learn and assign absolute binary predicate for individual data sample, the proposed RankGAN is able to analyze and rank a collection of human-written and machine-written sentences by giving a reference group. By viewing a set of data samples collectively and evaluating their quality through relative ranking scores, the discriminator is able to make better assessment which in turn helps to learn a better generator. The proposed RankGAN is optimized through the policy gradient technique. Experimental results on multiple public datasets clearly demonstrate the effectiveness of the proposed approach. In our recent work (Bubeck, Price, Razenshteyn, arXiv:1805.10204) we argued that adversarial examples in machine learning might be due to an inherent computational hardness of the problem. More precisely, we constructed a binary classification task for which (i) a robust classifier exists; yet no non-trivial accuracy can be obtained with an efficient algorithm in (ii) the statistical query model. In the present paper we significantly strengthen both (i) and (ii): we now construct a task which admits (i') a maximally robust classifier (that is it can tolerate perturbations of size comparable to the size of the examples themselves); and moreover we prove computational hardness of learning this task under (ii') a standard cryptographic assumption. In this paper, we focus on improving the proposal classification stage in the object detection task and present implicit negative sub-categorization and sink diversion to lift the performance by strengthening loss function in this stage. First, based on the observation that the “background” class is generally very diverse and thus challenging to be handled as a single indiscriminative class in existing state-of-the-art methods, we propose to divide the background category into multiple implicit sub-categories to explicitly differentiate diverse patterns within it. Second, since the ground truth class inevitably has low-value probability scores for certain images, we propose to add a “sink” class and divert the probabilities of wrong classes to this class when necessary, such that the ground truth label will still have a higher probability than other wrong classes even though it has low probability output. Additionally, we propose to use dilated convolution, which is widely used in the semantic segmentation task, for efficient and valuable context information extraction. Extensive experiments on PASCAL VOC 2007 and 2012 data sets show that our proposed methods based on faster R-CNN implementation can achieve state-of-the-art mAPs, i.e., 84.1 , 82.6 , respectively, and obtain 2.5 improvement on ILSVRC DET compared with that of ResNet. We introduce an adversarial learning framework, which we named KBGAN, to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graph datasets typically only contain positive facts, sampling useful negative training examples is a non-trivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts used by many previous works, but the majority of negative facts generated in this way can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. The objective of the generator is to generate difficult negative samples that can maximize their likeliness determined by the discriminator, while the discriminator minimizes its training loss. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TransE and TransD, each with assistance from one of the two probability-based models, DistMult and ComplEx. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.