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深度学习
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== 挑战与发展趋势 == 尽管成就斐然,深度学习仍面临诸多挑战:对大规模标注数据和算力的高度依赖、模型的"黑箱"特性导致可解释性不足(参见 [[AI 伦理与安全]])、对对抗样本缺乏鲁棒性,以及高昂的训练能耗与碳排放。未来的发展趋势包括自监督与无监督学习以降低数据依赖、模型轻量化与高效推理、多模态融合,以及将深度学习与符号推理、知识相结合以增强模型的推理能力。
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