Single-image haze removal is a long-standing hurdle for computer vision applications. Several works have been focused on transferring advances from image classification, detection, and segmentation to the niche of image dehazing, primarily focusing on contrastive learning and knowledge distillation. However, these approaches prove computationally expensive, raising concern regarding their applicability to on-the-edge use-cases. This work introduces a simple, lightweight, and efficient framework for single-image haze removal, exploiting rich "dark-knowledge" information from a lightweight pre-trained super-resolution model via the notion of heterogeneous knowledge distillation. We designed a feature affinity module to maximize the flow of rich feature semantics from the super-resolution teacher to the student dehazing network. In order to evaluate the efficacy of our proposed framework, its performance as a plug-and-play setup to a baseline model is examined. Our experiments are carried out on the RESIDE-Standard dataset to demonstrate the robustness of our framework to the synthetic and real-world domains. The extensive qualitative and quantitative results provided establish the effectiveness of the framework, achieving gains of upto 15\% (PSNR) while reducing the model size by $\sim$20 times.

Machine learning has proved useful in many software disciplines, including computer vision, speech and audio processing, natural language processing, robotics and some other fields. However, its applicability has been significantly hampered due its black-box nature and significant resource consumption. Performance is achieved at the expense of enormous computational resource and usually compromising the robustness and trustworthiness of the model. Recent researches have been identifying a lack of interactivity as the prime source of these machine learning problems. Consequently, interactive machine learning (iML) has acquired increased attention of researchers on account of its human-in-the-loop modality and relatively efficient resource utilization. Thereby, a state-of-the-art review of interactive machine learning plays a vital role in easing the effort toward building human-centred models. In this paper, we provide a comprehensive analysis of the state-of-the-art of iML. We analyze salient research works using merit-oriented and application/task oriented mixed taxonomy. We use a bottom-up clustering approach to generate a taxonomy of iML research works. Research works on adversarial black-box attacks and corresponding iML based defense system, exploratory machine learning, resource constrained learning, and iML performance evaluation are analyzed under their corresponding theme in our merit-oriented taxonomy. We have further classified these research works into technical and sectoral categories. Finally, research opportunities that we believe are inspiring for future work in iML are discussed thoroughly.

The rapid development of artificial intelligence (AI) technology has enabled large-scale AI applications to land in the market and practice. However, while AI technology has brought many conveniences to people in the productization process, it has also exposed many security issues. Especially, attacks against online learning vulnerabilities of chatbots occur frequently. Therefore, this paper proposes a semantics censorship chatbot system based on reinforcement learning, which is mainly composed of two parts: the Offensive semantics censorship model and the semantics purification model. Offensive semantics review can combine the context of user input sentences to detect the rapid evolution of Offensive semantics and respond to Offensive semantics responses. The semantics purification model For the case of chatting robot models, it has been contaminated by large numbers of offensive semantics, by strengthening the offensive reply learned by the learning algorithm, rather than rolling back to the early versions. In addition, by integrating a once-through learning approach, the speed of semantics purification is accelerated while reducing the impact on the quality of replies. The experimental results show that our proposed approach reduces the probability of the chat model generating offensive replies and that the integration of the few-shot learning algorithm improves the training speed rapidly while effectively slowing down the decline in BLEU values.

Deep learning-based source dehazing methods trained on synthetic datasets have achieved remarkable performance but suffer from dramatic performance degradation on real hazy images due to domain shift. Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains. To address these issues, we present a novel Source-Free Unsupervised Domain Adaptation (SFUDA) image dehazing paradigm, in which only a well-trained source model and an unlabeled target real hazy dataset are available. Specifically, we devise the Domain Representation Normalization (DRN) module to make the representation of real hazy domain features match that of the synthetic domain to bridge the gaps. With our plug-and-play DRN module, unlabeled real hazy images can adapt existing well-trained source networks. Besides, the unsupervised losses are applied to guide the learning of the DRN module, which consists of frequency losses and physical prior losses. Frequency losses provide structure and style constraints, while the prior loss explores the inherent statistic property of haze-free images. Equipped with our DRN module and unsupervised loss, existing source dehazing models are able to dehaze unlabeled real hazy images. Extensive experiments on multiple baselines demonstrate the validity and superiority of our method visually and quantitatively.

We propose a novel approach for semi-supervised learning (SSL) designed to overcome distribution shifts between training and real-world data arising in the keyword spotting (KWS) task. Shifts from training data distribution are a key challenge for real-world KWS tasks: when a new model is deployed on device, the gating of the accepted data undergoes a shift in distribution, making the problem of timely updates via subsequent deployments hard. Despite the shift, we assume that the marginal distributions on labels do not change. We utilize a modified teacher/student training framework, where labeled training data is augmented with unlabeled data. Note that the teacher does not have access to the new distribution as well. To train effectively with a mix of human and teacher labeled data, we develop a teacher labeling strategy based on confidence heuristics to reduce entropy on the label distribution from the teacher model; the data is then sampled to match the marginal distribution on the labels. Large scale experimental results show that a convolutional neural network (CNN) trained on far-field audio, and evaluated on far-field audio drawn from a different distribution, obtains a 14.3% relative improvement in false discovery rate (FDR) at equal false reject rate (FRR), while yielding a 5% improvement in FDR under no distribution shift. Under a more severe distribution shift from far-field to near-field audio with a smaller fully connected network (FCN) our approach achieves a 52% relative improvement in FDR at equal FRR, while yielding a 20% relative improvement in FDR on the original distribution.

In task-oriented dialogue (ToD), a user holds a conversation with an artificial agent  with the aim of completing a concrete task. Although this technology represents one of  the central objectives of AI and has been the focus of ever more intense research and  development efforts, it is currently limited to a few narrow domains (e.g., food ordering,  ticket booking) and a handful of languages (e.g., English, Chinese). This work provides an  extensive overview of existing methods and resources in multilingual ToD as an entry point  to this exciting and emerging field. We find that the most critical factor preventing the  creation of truly multilingual ToD systems is the lack of datasets in most languages for  both training and evaluation. In fact, acquiring annotations or human feedback for each  component of modular systems or for data-hungry end-to-end systems is expensive and  tedious. Hence, state-of-the-art approaches to multilingual ToD mostly rely on (zero- or  few-shot) cross-lingual transfer from resource-rich languages (almost exclusively English),  either by means of (i) machine translation or (ii) multilingual representations. These  approaches are currently viable only for typologically similar languages and languages with  parallel / monolingual corpora available. On the other hand, their effectiveness beyond these  boundaries is doubtful or hard to assess due to the lack of linguistically diverse benchmarks  (especially for natural language generation and end-to-end evaluation). To overcome this  limitation, we draw parallels between components of the ToD pipeline and other NLP tasks,  which can inspire solutions for learning in low-resource scenarios. Finally, we list additional  challenges that multilinguality poses for related areas (such as speech, fluency in generated  text, and human-centred evaluation), and indicate future directions that hold promise to  further expand language coverage and dialogue capabilities of current ToD systems. 

Tom Taulli has been developing software since the 1980s. In college, he started his first company, which focused on the development of e-learning systems. He created other companies as well, including Hypermart.net Along the way, Tom has written columns for online publications such as BusinessWeek.com, He also writes posts on Artificial Intelligence for Forbes.com

According to a report by New Media Consortium, adaptive learning (AL) and learning analytics are two crucial developments emerging in the educational technology market. Today, students pay more and more attention to individualized learning and instruction. If you are one of the higher ed institutions ramping up efforts to improve learning outcomes, implementing adaptive learning systems can be the potential solution. In this article at Hackernoon, Shannon Flynn explains how big data shapes AL. AL is an online educational system that focuses on understanding the student.

Even young babies are aware of the basic physics of everyday objects.Credit: Getty Inspired by research into how infants learn, computer scientists have created a program that can pick up simple physical rules about the behaviour of objects -- and express surprise when they seem to violate those rules. The results were published on 11 July in Nature Human Behaviour1. Developmental psychologists test how babies understand the motion of objects by tracking their gaze. When shown a video of, for example, a ball that suddenly disappears, the children express surprise, which researchers quantify by measuring how long the infants stare in a particular direction. Luis Piloto, a computer scientist at Google-owned company DeepMind in London, and his collaborators wanted to develop a similar test for artificial intelligence (AI).

You need to develop good programming skills if you wanna become a computer vision expert. There are lots of programming languages are available, that you can choose from. But, Python and R are the most suitable programming language for Computer Vision and Deep Learning. I would suggest you learn Python or R. So, if you are a beginner, I will recommend you, learn Python. Now, let's see the resources to learn Python and R. I am also going to list some free resources to learn R Programming.