Additionally, it is evident that the results in (ii) do not implytheconvergenceof f(xk) to0.

Sharpness-Aware Minimization (SAM) has attracted significant attention for its effectiveness in improving generalization across various tasks. However, its underlying principles remain poorly understood.

Sparse training stands as a landmark approach in addressing the considerable training resource demands imposed by the continuously expanding size of Deep Neural Networks (DNNs). However, the training of a sparse DNN encounters great challenges in achieving optimal generalization ability despite the efforts from the state-of-the-art sparse training methodologies. To unravel the mysterious reason behind the difficulty of sparse training, we connect the network sparsity with neural loss functions structure, and identify the cause of such difficulty lies in chaotic loss surface. In light of such revelation, we propose $S^{2} - SAM$, characterized by a **S**ingle-step **S**harpness_**A**ware **M**inimization that is tailored for **S**parse training.

We refer to the proposed solution as SA3D, for Segment Anything in 3D. It is only required to provide a manual segmentation prompt ( e.g., rough points) for the target object in a single view, which is used to generate its 2D mask in this view with SAM.

The Segmentation Anything Model (SAM) requires labor-intensive data labeling. We present Unsupervised SAM (UnSAM) for promptable and automatic whole-image segmentation that does not require human annotations. UnSAM utilizes a divide-and-conquer strategy to "discover" the hierarchical structure of visual scenes. For all pixels within a segment, a bottom-up clustering method is employed to iteratively merge them into larger groups, thereby forming a hierarchical structure. These unsupervised multi-granular masks are then utilized to supervise model training.

Visual foundation models have achieved remarkable results in zero-shot image classification and segmentation, but zero-shot change detection remains an open problem. In this paper, we propose the segment any change models (AnyChange), a new type of change detection model that supports zero-shot prediction and generalization on unseen change types and data distributions.AnyChange is built on the segment anything model (SAM) via our training-free adaptation method, bitemporal latent matching.By revealing and exploiting intra-image and inter-image semantic similarities in SAM's latent space, bitemporal latent matching endows SAM with zero-shot change detection capabilities in a training-free way. We also propose a point query mechanism to enable AnyChange's zero-shot object-centric change detection capability.We perform extensive experiments to confirm the effectiveness of AnyChange for zero-shot change detection.AnyChange sets a new record on the SECOND benchmark for unsupervised change detection, exceeding the previous SOTA by up to 4.4\% F _1 score, and achieving comparable accuracy with negligible manual annotations (1 pixel per image) for supervised change detection.

Can we modify the training data distribution to encourage the underlying optimization method toward finding solutions with superior generalization performance on in-distribution data? In this work, we approach this question for the first time by comparing the inductive bias of gradient descent (GD) with that of sharpness-aware minimization (SAM). By studying a two-layer CNN, we rigorously prove that SAM learns different features more uniformly, particularly in early epochs. That is, SAM is less susceptible to simplicity bias compared to GD. We also show that examples constraining features that are learned early are separable from the rest based on the model's output.

Sam's Club is going register-free and introducing an all-digital, AI-powered shopping experience for its customers, a move that has privacy advocates worried that the new AI tool could be used to unfairly target some customers with higher-priced items based on their shopping habits. The all-digital approach started with the reconstruction of a Sam's Club in Grapevine, a suburb of Dallas, that was severely damaged in 2022 by a tornado. When the retail location opened two years later it was the first of its kind to ditch its registers for a "Scan and Go" program that allowed customers to scan each item placed in their physical cart and pay through a mobile app. This program has since been piloted in nine Dallas metro locations and one store in Missouri, Retail Dive reported. Instead of handing a receipt to a Sam's Club employee to review before leaving the store, customers walk through an arch that's equipped with AI-powered cameras to capture images of the items in the cart and electronically match them with the items paid for through the app. Sam's Club did not disclose when the AI technology would be coming to California stores but Sam's Club has outlets in Torrance, Fountain Valley, El Monte and Riverside.

On Monday, a developer using the popular AI-powered code editor Cursor noticed something strange: Switching between machines instantly logged them out, breaking a common workflow for programmers who use multiple devices. When the user contacted Cursor support, an agent named "Sam" told them it was expected behavior under a new policy. But no such policy existed, and Sam was a bot. The AI model made the policy up, sparking a wave of complaints and cancellation threats documented on Hacker News and Reddit. This marks the latest instance of AI confabulations (also called "hallucinations") causing potential business damage.