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Cosine annealing (cosine decay) is a learning rate scheduling strategy that gradually decays the learning rate from its initial value toward near zero following a cosine function curve.
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9/26/2026
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UnverifiedCosine annealing allows the learning rate to smoothly decrease along a cosine curve to near zero and periodically restart, which was formalized by the SGDR algorithm.67% similarUnverified表示坍缩指模型将所有输入映射到同一点或低维流形，使损失趋近于零但毫无意义，是自监督学习的核心挑战67% similarUnverifiedThe cosine annealing learning rate scheduling strategy was proposed by Loshchilov and Hutter in 2017 and smoothly decays the learning rate from its initial value to near zero following a cosine function.66% similarUnverifiedThe parameter update rule for gradient descent is θ_{t+1} = θ_t - η·∇L(θ_t), where η is the learning rate, and updating parameters in the direction of the negative gradient reduces the loss function most rapidly.65% similar
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