liqiang
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48 lines
1.6 KiB
JSON
48 lines
1.6 KiB
JSON
{
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"overall_score": 75,
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"pass": false,
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"issues": [
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{
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"page": 3,
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"issue_type": "overcrowded",
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"severity": "medium",
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"suggestion": "将Recurrent Models和Convolutional Models分为两页展示，或每个部分保留2个最关键要点"
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},
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{
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"page": 6,
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"issue_type": "overcrowded",
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"severity": "high",
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"suggestion": "将Encoder和Decoder架构分为两页，每部分控制在4个要点以内，删除重复的\"Residual connections + layer normalization\""
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},
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{
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"page": 10,
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"issue_type": "overcrowded",
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"severity": "medium",
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"suggestion": "将Training Setup拆分为数据/硬件和优化策略两页，或删除学习率公式的数学表达仅保留文字描述"
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},
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{
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"page": 12,
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"issue_type": "readability",
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"severity": "low",
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"suggestion": "将底部总结文字拆分为两个独立项目符号，使用更清晰的分隔符替代当前的黑色方块符号"
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},
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{
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"page": 14,
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"issue_type": "image_size",
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"severity": "medium",
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"suggestion": "放大注意力可视化图表，确保轴标签文字清晰可辨，或裁剪部分padding区域聚焦核心可视化内容"
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},
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{
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"page": 15,
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"issue_type": "image_size",
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"severity": "medium",
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"suggestion": "放大指代消解可视化图表，增加线条对比度，或仅保留最具代表性的1-2个注意力头可视化"
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},
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{
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"page": 16,
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"issue_type": "overcrowded",
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"severity": "medium",
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"suggestion": "将Limitations和Future Work分为两页，或每个部分精简为2-3个核心要点"
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}
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]
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} |