We study adapting trained object detectors to unseen domains manifesting significant variations of object appearance, viewpoints and backgrounds. Most current methods align domains by either using image or instance-level feature alignment in an adversarial fashion. This often suffers due to the presence of unwanted background and as such lacks class-specific alignment. A common remedy to promote class-level alignment is to use high confidence predictions on the unlabelled domain as pseudo labels. These high confidence predictions are often fallacious since the model is poorly calibrated under domain shift. In this paper, we propose to leverage model's predictive uncertainty to strike the right balance between adversarial feature alignment and class-level alignment.

Hyperchat AI is a novel agentic technology that enables thoughtful conversations among networked human groups of potentially unlimited size. It allows large teams to discuss complex issues, brainstorm ideas, surface risks, assess alternatives and efficiently converge on optimized solutions that amplify the group's Collective Intelligence (CI). A formal study was conducted to quantify the forecasting accuracy of human groups using Hyperchat AI to conversationally predict the outcome of Major League Baseball (MLB) games. During an 8-week period, networked groups of approximately 24 sports fans were tasked with collaboratively forecasting the winners of 59 baseball games through real-time conversation facilitated by AI agents. The results showed that when debating the games using Hyperchat AI technology, the groups converged on High Confidence predictions that significantly outperformed Vegas betting markets. Specifically, groups were 78% accurate in their High Confidence picks, a statistically strong result vs the Vegas odds of 57% (p=0.020). Had the groups bet against the spread (ATS) on these games, they would have achieved a 46% ROI against Vegas betting markets. In addition, High Confidence forecasts that were generated through above-average conversation rates were 88% accurate, suggesting that real-time interactive deliberation is central to amplified accuracy.

We study adapting trained object detectors to unseen domains manifesting significant variations of object appearance, viewpoints and backgrounds. Most current methods align domains by either using image or instance-level feature alignment in an adversarial fashion. This often suffers due to the presence of unwanted background and as such lacks class-specific alignment. A common remedy to promote class-level alignment is to use high confidence predictions on the unlabelled domain as pseudo labels. These high confidence predictions are often fallacious since the model is poorly calibrated under domain shift.

AI and ML models have already found many applications in critical domains, such as healthcare and criminal justice. However, fully automating such high-stakes applications can raise ethical or fairness concerns. Instead, in such cases, humans should be assisted by automated systems so that the two parties reach a joint decision, stemming out of their interaction. In this work we conduct an empirical study to identify how uncertainty estimates and model explanations affect users' reliance, understanding, and trust towards a model, looking for potential benefits of bringing the two together. Moreover, we seek to assess how users' behaviour is affected by their own self-confidence in their abilities to perform a certain task, while we also discuss how the latter may distort the outcome of an analysis based on agreement and switching percentages.

Classifiers used in the wild, in particular for safety-critical systems, should not only have good generalization properties but also should know when they don't know, in particular make low confidence predictions far away from the training data. We show that ReLU type neural networks which yield a piecewise linear classifier function fail in this regard as they produce almost always high confidence predictions far away from the training data. For bounded domains like images we propose a new robust optimization technique similar to adversarial training which enforces low confidence predictions far away from the training data. We show that this technique is surprisingly effective in reducing the confidence of predictions far away from the training data while maintaining high confidence predictions and similar test error on the original classification task compared to standard training.

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