In our Dataset Bias Analysis project, we conducted a literature review and empirical study on dataset biases in image classification. Building on the work of Torralba and Efros in 2011, we reproduced their Histogram of Gradients (HOG) and Support Vector Machines (SVM) results and compared them with modern Convolutional Neural Networks (CNNs) using the caltech101, MSRC, and PASCAL VOC-2007 datasets. Our study focused on identifying selection, framing, and labeling biases, and we found that both SVM and CNN models showed significant performance drops when trained and tested on different datasets, highlighting the persistent problem of dataset bias affecting model generalization.
