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"Layer Recycling and Fine-tuning Efficiency"
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[
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"Layer Recycling and Fine-tuning Efficiency"
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Layer Recycling and Fine-tuning Efficiency"
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(1) | [
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[
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] | p | For example, the question answering task suffers from a more significant degradation in performance with 50% of the layers recycled, | [
"articles",
"articles/embedding-recycler"
] | 4,588 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"How layer recycling works"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3) | [
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] | 4,589 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"How layer recycling works"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3) | [
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] | p | so they suggest determining the level of caching based on the task at hand. | [
"articles",
"articles/embedding-recycler"
] | 4,590 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"How layer recycling works"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(3) | [
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"How layer recycling works"
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"How layer recycling works"
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"articles",
"articles/embedding-recycler"
] | 4,596 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"How layer recycling works"
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] | p | Thanks to this flexibility, we were able to run a variety of experiments in different setups, | [
"articles",
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[
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"How layer recycling works"
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] | p | We conducted different experiments to test the performance with: | [
"articles",
"articles/embedding-recycler"
] | 4,602 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiments"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(1) | [
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"articles",
"articles/embedding-recycler"
] | 4,603 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiments"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(2) | [
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] | li | Different numbers of samples in the dataset for training and fine-tuning for similar cars search. | [
"articles",
"articles/embedding-recycler"
] | 4,604 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiments"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(1) > li:nth-of-type(3) | [
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] | p | The easiest way of caching layers in Quaterion is to compose a TrainableModel | [
"articles",
"articles/embedding-recycler"
] | 4,607 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Easy layer recycling with Quaterion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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"articles",
"articles/embedding-recycler"
] | 4,610 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Easy layer recycling with Quaterion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(6) | [
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"articles/embedding-recycler"
] | 4,611 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Easy layer recycling with Quaterion"
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] | p | This trick lets us finetune one more layer from the base model as a part of the EncoderHead | [
"articles",
"articles/embedding-recycler"
] | 4,612 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Easy layer recycling with Quaterion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(7) | [
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] | p | The paper states that recycling 50% of the layers yields little to no loss in performance when compared to full fine-tuning. | [
"articles",
"articles/embedding-recycler"
] | 4,615 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(8) | [
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] | p | In this setup, we compared performances of four methods: | [
"articles",
"articles/embedding-recycler"
] | 4,616 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(1) | [
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] | 4,617 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(2) | [
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] | li | Move one of the four residual blocks EncoderHead and train it together with the head layer while freezing the rest (75% layer recycling). | [
"articles",
"articles/embedding-recycler"
] | 4,618 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(3) | [
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"articles",
"articles/embedding-recycler"
] | 4,619 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > ol:nth-of-type(2) > li:nth-of-type(4) | [
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9) | [
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"articles",
"articles/embedding-recycler"
] | 4,621 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9) | [
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(9) | [
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] | p | The baseline score with ResNet34 is 0.106. | [
"articles",
"articles/embedding-recycler"
] | 4,623 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 1: Percentage of layers recycled"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(10) | [
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] | p | As is seen in the table, the performance in 50% layer recycling is very close to that in full training. | [
"articles",
"articles/embedding-recycler"
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"Experiment 1: Percentage of layers recycled"
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[
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[
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] | p | In the second experiment setup, we compared performances of fine-tuning strategies with different dataset sizes. | [
"articles",
"articles/embedding-recycler"
] | 4,629 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 2: Amount of available data"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(11) | [
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] | p | We sampled 50% of the training set randomly while still evaluating models on the whole validation set. | [
"articles",
"articles/embedding-recycler"
] | 4,630 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 2: Amount of available data"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12) | [
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[
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] | p | On the other hand, the level of degradation in training only EncoderHead is really small when compared to others. | [
"articles",
"articles/embedding-recycler"
] | 4,633 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 2: Amount of available data"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12) | [
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] | p | When we further reduce the dataset size, full training becomes untrainable at some point, | [
"articles",
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] | 4,634 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 2: Amount of available data"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(12) | [
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"articles",
"articles/embedding-recycler"
] | 4,635 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > h2:nth-of-type(6) | [
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[
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"Experiment 3: Layer recycling in question answering"
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 3: Layer recycling in question answering"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(13) | [
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] | p | To this end, we set up an experiment with the code from the Question Answering with Similarity Learning tutorial. | [
"articles",
"articles/embedding-recycler"
] | 4,640 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 3: Layer recycling in question answering"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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] | p | In this task, 50% layer recycling can still do a good job with only a small drop in performance when compared to full training. | [
"articles",
"articles/embedding-recycler"
] | 4,641 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Experiment 3: Layer recycling in question answering"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(14) | [
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[
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"Experiment 3: Layer recycling in question answering"
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"Experiment 3: Layer recycling in question answering"
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[
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"Experiment 3: Layer recycling in question answering"
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Conclusion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | We set up several experiments to test layer recycling under different constraints | [
"articles",
"articles/embedding-recycler"
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Conclusion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | and confirmed that layer recycling yields varying performances with different tasks and domains. | [
"articles",
"articles/embedding-recycler"
] | 4,649 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Conclusion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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[
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"Conclusion"
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Conclusion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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"articles",
"articles/embedding-recycler"
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Conclusion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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] | p | and luckily Quaterion is flexible enough to run such experiments quickly. | [
"articles",
"articles/embedding-recycler"
] | 4,657 |
[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Conclusion"
] | /articles/embedding-recycler/ | html > body > div:nth-of-type(1) > div:nth-of-type(1) > div > section > article > p:nth-of-type(15) | [
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"Layer Recycling and Fine-tuning Efficiency - Qdrant",
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"Layer Recycling and Fine-tuning Efficiency - Qdrant",
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[
"Layer Recycling and Fine-tuning Efficiency - Qdrant",
"Conclusion"
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] | li | Your classes may change quickly —e.g., new classes may be added over time, | [
"articles",
"articles/cars-recognition"
] | 4,671 |
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