Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

Prior works have found it beneficial to combine provably noise-robust loss functions e.g., mean absolute error (MAE) with standard categorical loss function e.g. cross entropy (CE) to improve their learnability. Here, we propose to use Jensen-Shannon divergence as a noise-robust loss function and show that it interestingly interpolate between CE and MAE with a controllable mixing parameter. Furthermore, we make a crucial observation that CE exhibit lower consistency around noisy data points. Based on this observation, we adopt a generalized version of the Jensen-Shannon divergence for multiple distributions to encourage consistency around data points. Using this loss function, we show state-of-the-art results on both synthetic (CIFAR), and real-world (e.g., WebVision) noise with varying noise rates.Comment: Neural Information Processing Systems (NeurIPS 2021

Logistic-Normal Likelihoods for Heteroscedastic Label Noise

A natural way of estimating heteroscedastic label noise in regression is to model the observed (potentially noisy) target as a sample from a normal distribution, whose parameters can be learned by minimizing the negative log-likelihood. This formulation has desirable loss attenuation properties, as it reduces the contribution of high-error examples. Intuitively, this behavior can improve robustness against label noise by reducing overfitting. We propose an extension of this simple and probabilistic approach to classification that has the same desirable loss attenuation properties. Furthermore, we discuss and address some practical challenges of this extension. We evaluate the effectiveness of the method by measuring its robustness against label noise in classification. We perform enlightening experiments exploring the inner workings of the method, including sensitivity to hyperparameters, ablation studies, and other insightful analyses

Deep Double Descent via Smooth Interpolation

The ability of overparameterized deep networks to interpolate noisy data, while at the same time showing good generalization performance, has been recently characterized in terms of the double descent curve for the test error. Common intuition from polynomial regression suggests that overparameterized networks are able to sharply interpolate noisy data, without considerably deviating from the ground-truth signal, thus preserving generalization ability. At present, a precise characterization of the relationship between interpolation and generalization for deep networks is missing. In this work, we quantify sharpness of fit of the training data interpolated by neural network functions, by studying the loss landscape w.r.t. to the input variable locally to each training point, over volumes around cleanly- and noisily-labelled training samples, as we systematically increase the number of model parameters and training epochs. Our findings show that loss sharpness in the input space follows both model- and epoch-wise double descent, with worse peaks observed around noisy labels. While small interpolating models sharply fit both clean and noisy data, large interpolating models express a smooth loss landscape, where noisy targets are predicted over large volumes around training data points, in contrast to existing intuition

Security Enhancement of E-Voting System

The term E-Votingǁ is used in variety of different ways and it encompasses all voting techniques involving electronic voting equipments, voting over the internet, using electronic booths in polling stations and sometimes even counting of paper ballots. A voting system that can be proven correct has many concerns. The basic reasons for a government to use electronic systems are to increase election activities and to reduce the election expenses. Still there is some scope of work in electronic voting system in terms of checking the authenticity of voters and securing electronic voting machine from miscreants. Biometrics is automated tool for verifying the identity of a person based on a physiological or behavioral characteristic. It has the capability to reliably distinguish between an authorized person and an imposter. Since biometric characteristics are distinctive, can not be forgotten or lost and the person to be authenticated needs to be physically present at the point of identification, biometrics is inherently more reliable and more capable than traditional knowledgebased and token-based techniques. In this paper, we have proposed a model to enhance the security of electronic voting system by incorporating fast and accurate biometric technique to prevent an unauthorized person to vote

Tinnitus

Tinnitus is a prevalent experience and, for those who are troubled by it, it can be debilitating. Risk factors include hearing loss, ototoxic medication, head injury and depression, and at presentation the possibility of otologic disease and of anxiety/depression should be considered. Effective drug treatments have proven elusive, though this is a vibrant theme in tinnitus research. Surgical intervention for any otological pathology associated with tinnitus may be effective for that condition, but the tinnitus may persist. Presently available treatments include the provision of hearing aids when a hearing loss is identified (even when mild or unilateral), wide band sound therapy and counselling. In some patients, cognitive behavioural therapy (CBT) is indicated though availability of tinnitus specific CBT is limited in the UK. Of these treatments the evidence base is strongest for a combination of sound therapy and CBT based counselling, though clinical trials are constrained by the heterogeneity of the tinnitus patient population. Research into mechanisms of tinnitus and effective treatments now abounds, and progress is keenly anticipated

Childrens experiences of radiographic examinations and what as a radiographer you should take with you when meeting with acute ill children - A litterature review

Bakgrund: Barn kommer ofta akut till röntgen och som legitimerad röntgensjuksköterska har man som skyldighet värna om en god omvårdnad och hålla stråldoser nere. Barn är mycket strålkänsliga och har högre risk att drabbas av stokastiska effekter av strålning än vuxna. När ett barn är stressat och otryggt kan detta försvåra undersökningar och bidra till onödigt förhöjda stråldoser. Med sin kommunikation och genom avledning kan man hjälpa barnet att känna trygghet. Syfte: Att med denna studie ta reda på barns upplevelser av akuta röntgenundersökningar och vad man som röntgensjuksköterska bör ha med sig när man träffar barn på röntgen. Metod: Detta examensarbete är en litteraturstudie där 13 artiklar av kvalitativ och kvantitativ ansats har analyserats för att svara till arbetets syfte. Resultat: I resultatet framkom två huvudsakliga känslor barn hade i samband med sin akuta röntgenundersökning. Känslan av rädsla och oro, och känslan av trygghet. Saker som kan påverka dessa upplevelser visade sig vara röntgensjuksköterskan sätt att informera och kommunicera, föräldrarnas närvaro och miljön på röntgen. Det man utifrån barnens upplevelser som röntgensjuksköterska bör ha med sig var tänka på sin teknik när man informerar och ge den till barnet på ett sätt som barnet förstår. Att distrahera kan hjälpa till att få barnet att tänka på något annat än sin smärta. Att ge barnet uppmärksamhet var också något som ingav trygghet likaså att verka för god omvårdnad och se till barnet och dess behov som en enskild individ. Konklusion: Man bör ta lärdom av barnens upplevelser på röntgen i ett fortsatt arbete med individanpassad omvårdnad. Det är en röntgensjuksköterskas skyldighet att sträva efter att barn skall få en positiv upplevelse på röntgen samtidigt som stråldoserna skall hållas nere. Om man får barnet att känna trygghet kan man därmed uppnå målet med en god omvårdnad och en säker vård