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New loss smoothing technique improves neural network adaptation under distribution shift

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced a technique called "loss smoothing" to improve the adaptation of neural networks when faced with distribution shifts. This method involves gradually interpolating between the original source training objective and the new target objective at the beginning of the adaptation process. By doing so, it helps preserve valuable features learned from the source data while still allowing the model to specialize for the new task. Experiments across various domains, including vision adaptation, reinforcement learning, and language model fine-tuning, demonstrate that loss smoothing consistently enhances performance. AI

IMPACT This method could lead to more robust and efficient fine-tuning of AI models across various applications.

RANK_REASON The cluster contains an academic paper detailing a new method for neural network adaptation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New loss smoothing technique improves neural network adaptation under distribution shift

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The cluster contains an academic paper detailing a new method for neural network adaptation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Darshan Patil, Ekaterina Lobacheva, Razvan Pascanu, Sarath Chandar ·

    Loss Smoothing for Stable Adaptation Under Distribution Shift

    arXiv:2607.00634v1 Announce Type: cross Abstract: In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods typically optimize the target objective directly, inducing an abrupt change from t…

  2. arXiv cs.AI TIER_1 English(EN) · Sarath Chandar ·

    Loss Smoothing for Stable Adaptation Under Distribution Shift

    In settings such as fine-tuning and reinforcement learning, neural networks are often adapted under distribution shift. Standard adaptation methods typically optimize the target objective directly, inducing an abrupt change from the source training objective. This abrupt transiti…

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