Abhishek Kumar

Mintong Kang, Vinayshekhar Bannihatti Kumar, Shamik Roy et al.

Text-to-image diffusion models often exhibit biases toward specific demographic groups, such as generating more males than females when prompted to generate images of engineers, raising ethical concerns and limiting their adoption. In this paper, we tackle the challenge of mitigating generation bias towards any target attribute value (e.g., "male" for "gender") in diffusion models while preserving generation quality. We propose FairGen, an adaptive latent guidance mechanism which controls the generation distribution during inference. In FairGen, a latent guidance module dynamically adjusts the diffusion process to enforce specific attributes, while a memory module tracks the generation statistics and steers latent guidance to align with the targeted fair distribution of the attribute values. Furthermore, we address the limitations of existing datasets by introducing the Holistic Bias Evaluation (HBE) benchmark, which covers diverse domains and incorporates complex prompts to assess bias more comprehensively. Extensive evaluations on HBE and Stable Bias datasets demonstrate that FairGen outperforms existing bias mitigation approaches, achieving substantial bias reduction (e.g., 68.5% gender bias reduction on Stable Diffusion 2). Ablation studies highlight FairGen's ability to flexibly control the output distribution at any user-specified granularity, ensuring adaptive and targeted bias mitigation.

Abhishek Kumar, Suresh Chandra Gupta

Cluster analysis is one of the primary data analysis technique in data mining and K-means is one of the commonly used partitioning clustering algorithm. In K-means algorithm, resulting set of clusters depend on the choice of initial centroids. If we can find initial centroids which are coherent with the arrangement of data, the better set of clusters can be obtained. This paper proposes a method based on the Dissimilarity Tree to find, the better initial centroid as well as every bit more accurate cluster with less computational time. Theory analysis and experimental results indicate that the proposed method can effectively improve the accuracy of clusters and reduce the computational complexity of the K-means algorithm.

Amit Kumar Mishra, Abhishek Kumar, Dipankar Deb

In this paper, we introduce a cerebral cortex inspired architecture for robots in which we have mapped hierarchical cortical representation of human brain to logic flow and decision making process. Our work focuses on the two major features of human cognitive process, viz. the perception action cycle and its hierarchical organization, and the decision making process. To prove the effectiveness of our proposed method, we incorporated this architecture in our robot which we named as Cognitive Insect Robot inspired by Brain Architecture (CIRBA). We have extended our research to the implementation of this cognitive architecture of CIRBA in multiple robots and have analyzed the level of cognition attained by them