Overwriting Pretrained Bias with Finetuning Data

Transfer learning is beneficial by allowing the expressive features of models pretrained on large-scale datasets to be finetuned for the target task of smaller, more domain-specific datasets. However, there is a concern that these pretrained models may come with their own biases which would propagate into the finetuned model. In this work, we investigate bias when conceptualized as both spurious correlations between the target task and a sensitive attribute as well as underrepresentation of a particular group in the dataset. Under both notions of bias, we find that (1) models finetuned on top of pretrained models can indeed inherit their biases, but (2) this bias can be corrected for through relatively minor interventions to the finetuning dataset, and often with a negligible impact to performance. Our findings imply that careful curation of the finetuning dataset is important for reducing biases on a downstream task, and doing so can even compensate for bias in the pretrained model.Comment: ICCV 2023 Ora

Social work education as a catalyst for social change and social development: case study of a Master of Social Work Program in China

In response to the urgent need for professionally trained social workers to help in alleviating emerging social problems in China after the introduction of the market economy, the Hong Kong Polytechnic University and the Peking University launched a Master of Social Work (China) Program for social work educators in 2000, with the aim of developing a critical mass of social work educators to take up the future leadership in developing social work and social work education in China. To date, seven cohorts of over 230 students consisting of social work educators, NGO and government officials have been admitted to the program, and graduates of the program are playing a pivotal role in spearheading the development of social work education and fostering social development through the process. In this paper, the authors will present the vision and mission of the Master of Social Work (MSW) Program, the teaching and learning strategies adopted, and the ways in which the program has facilitated social change and social development through its educational process

Gender Artifacts in Visual Datasets

Gender biases are known to exist within large-scale visual datasets and can be reflected or even amplified in downstream models. Many prior works have proposed methods for mitigating gender biases, often by attempting to remove gender expression information from images. To understand the feasibility and practicality of these approaches, we investigate what $\textit{gender artifacts}$ exist within large-scale visual datasets. We define a $\textit{gender artifact}$ as a visual cue that is correlated with gender, focusing specifically on those cues that are learnable by a modern image classifier and have an interpretable human corollary. Through our analyses, we find that gender artifacts are ubiquitous in the COCO and OpenImages datasets, occurring everywhere from low-level information (e.g., the mean value of the color channels) to the higher-level composition of the image (e.g., pose and location of people). Given the prevalence of gender artifacts, we claim that attempts to remove gender artifacts from such datasets are largely infeasible. Instead, the responsibility lies with researchers and practitioners to be aware that the distribution of images within datasets is highly gendered and hence develop methods which are robust to these distributional shifts across groups.Comment: ICCV 202

Pd-Catalyzed de Novo Assembly of Diversely Substituted Indole-Fused Polyheterocycles

Here we describe a facile, tandem synthetic route for indolo[3,2-c]quinolinones, a class of natural alkaloid analogues of high biological significance. A Ugi four-component reaction with indole-2-carboxylic acid and an aniline followed by a Pd-catalyzed cyclization yields tetracyclic indoloquinolines in good to moderate yields. Commercially available building blocks yield highly diverse analogues in just two simple steps

Reply to : Cause or consequence?

Funding AstraZeneca funded the SABINA III study; was involved in the study design, protocol development, study conduct and statistical analysis; and was given the opportunity to review this manuscript before submission. Publication support was provided by Michelle Rebello, PhD, of Cactus Life Sciences and funded by AstraZeneca.Peer reviewedPostprin