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Improving Diffusion-Based Image Synthesis with Context Prediction Ling Y ang
Diffusion models are a new class of generative models, and have dramatically promoted image generation with unprecedented quality and diversity.
Virtual Class Enhanced Discriminative Embedding Learning
Binghui Chen, Weihong Deng, Haifeng Shen
Neural Information Processing Systems http://nips.cc/
Predicting the Politics of an Image Using Webly Supervised Data
Christopher Thomas, Adriana Kovashka
Neural Information Processing Systems http://nips.cc/
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation
Risto Vuorio, Shao-Hua Sun, Hexiang Hu, Joseph J. Lim
Neural Information Processing Systems http://nips.cc/
Predictive Attractor Models
The task of sequential memory is considered challenging for models operating under biological constraints (i.e., local synaptic computations) for many reasons, including catastrophic forgetting,
Disney accuses ByteDance of 'virtual smash-and-grab' when using copyrighted works to train its AI
Samsung Galaxy Unpacked 2026 is Feb. 25 Valve's Steam Machine: Everything we know Even though ByteDance just released Seedance 2.0 on Thursday, it's already earned praise, but also indignation from Hollywood studios, when it comes to its AI-generating capabilities. With the strong early momentum, Seedance has already found itself in hot water with one of the largest media companies in the world. However, it's not the first time that Disney has threatened legal action against an AI company, since Character.AI received a cease-and-desist letter for the same offense in September. On the other hand, Disney partnered with OpenAI in a three-year licensing agreement that allows the AI giant to generate images and videos using that highly sought-after intellectual property. By subscribing, you are agreeing to Engadget's Terms and Privacy Policy .
REBEL: Reinforcement Learning via Regressing Relative Rewards Zhaolin Gao 1, Jonathan D. Chang
While originally developed for continuous control problems, Proximal Policy Optimization (PPO) has emerged as the work-horse of a variety of reinforcement learning (RL) applications, including the fine-tuning of generative models. Unfortunately, PPO requires multiple heuristics to enable stable convergence (e.g.
NetHack is Hard to Hack
In this work, we conduct a comprehensive study of NetHack and examine various learning mechanisms to enhance the performance of neural models.