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Research map / Learning theory and algorithms / Prediction and decision losses

Curriculum Learning by Dynamic Instance Hardness

NeurIPS 2020 · Learning theory and algorithms › Prediction and decision losses · approach: Active learning and subset selection · 43 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
DIH-guided curriculum (DIHCL-Exp, Loss) instead of random mini-batchesSGD · CIFAR10test accuracy: 96.18 -> 97.03Helped
DIH-guided curriculum (DIHCL-Exp, Loss) instead of random mini-batchesSGD · CIFAR100test accuracy: 79.64 -> 82.23Helped
DIH-guided curriculum (DIHCL-Exp, dLoss) instead of random mini-batchesSGD · STL10test accuracy: 86.06 -> 89.41Helped
DIH-guided curriculum (DIHCL-Beta, Flip) instead of random mini-batchesSGD · ImageNettest accuracy: 75.04 -> 76.33Helped
DIH-guided curriculum instead of MCL (instantaneous loss)MCL · Food-101test accuracy: 84.18 -> 84.94Helped
DIH-guided curriculum (DIHCL-Exp, dLoss) instead of MCLMCL · Aircrafttest accuracy: 75.28 -> 79.72Helped
self-paced learning instead of random mini-batchesSGD · CIFAR10test accuracy: 96.18 -> 93.55Hurt
DIHCL-Exp dLoss instead of Rand Loss variant (instantaneous choice)DIHCL · CIFAR10test accuracy: 96.76 -> 96.40Mixed
change discount factor gamma from 0.95 to 0.9 or 0.8 (ablation)DIHCL-Rand Loss · CIFAR10test accuracy: 96.76 vs 96.75 vs 96.78No clear effect
variants of DIHCL: exponential decay LR, T0=3, increasing budget, smaller gamma_k, 40% noisy labels (ablation)DIHCL · CIFAR10test accuracyHelped

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