Transfer Learning

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Transfer Learning is Fine-tune a pre-trained model on your specific dataset.

19 facts·12 predicates·5 sources·2 in dispute

Mostly:rdf:type(6), involves(1), uses for(1)

Inbound mentions (7)

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`hasGoal`Has Goal(2)

Comparison Studyex:comparison-study
Study Comparisonex:study-comparison

`employs`Employs(1)

Training Scriptex:training-script

`enable`Enable(1)

Pre Trained Modelsex:pre-trained-models

`hasHighPerformanceFor`Has High Performance for(1)

Birdnetex:birdnet

`hasPart`Has Part(1)

Common Image Processing and Deep Learning Techniques for Medical Image Analysisex:common-image-processing-and-deep-learning-techniques-for-medical-image-analysis

`highPerformanceFor`High Performance for(1)

Evaluated Embedding Modelsex:evaluated-embedding-models

Other facts (17)

The long tail: predicates that appear too rarely to warrant their own section. Filter or scroll to find a specific one. Each row links to its source.

17 facts

Predicate	Value	Ref
Rdf:type	Learning Technique	[1]
Rdf:type	Model Adaptation Strategy	[2]
Rdf:type	Machine Learning Paradigm	[3]
Rdf:type	Learning Paradigm	[4]
Rdf:type	Machine Learning Paradigm	[5]
Rdf:type	Methodology	[5]
Involves	Pre Trained Model	[1]
Uses for	Related Task	[1]
Requires	Fine Tuning	[1]
Fine Tuned on	Labeled Data	[1]
Leverages	Knowledge From Larger Dataset	[1]
Improves	Performance on Smaller Dataset	[1]
Leverages From	Larger Dataset	[1]
Improves for	Smaller Labeled Dataset	[1]
Description	Fine-tune a pre-trained model on your specific dataset	[2]
Utility	allow for quick iteration	[5]
Is Valuable Tool for	Marine Bioacoustics	[5]

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19 undated

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typebeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:LearningTechnique

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labelbeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

Transfer Learning

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involvesbeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:pre-trained-model

—

usesForbeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:related-task

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requiresbeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:fine-tuning

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fineTunedOnbeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:labeled-data

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leveragesbeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:knowledge-from-larger-dataset

—

improvesbeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:performance-on-smaller-dataset

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leveragesFrombeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:larger-dataset

—

improvesForbeam/2ddf9036-a5aa-42e2-acdc-0f042de6c505

ex:smaller-labeled-dataset

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descriptionbeam/dec138b8-3361-428f-b049-8ef1e4b6719e

Fine-tune a pre-trained model on your specific dataset

—

typebeam/dec138b8-3361-428f-b049-8ef1e4b6719e

ex:ModelAdaptationStrategy

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typebeam/f266ef67-57dd-4b1f-b9ab-661effb75c4b

ex:MachineLearningParadigm

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typebeam/0e4dede6-52a5-49ce-a450-4813d1738359

ex:LearningParadigm

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typetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

ex:MachineLearningParadigm

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utilitytp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

allow for quick iteration

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isValuableToolFortp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

ex:marine-bioacoustics

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typetp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

ex:Methodology

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labeltp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims

transfer-learning

References (5)

5 references

[1]2ddf9036 A5aa 42e2 Acdc 0f042de6c50510 facts
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  Semi-supervised learning combines a small amount of labeled data with a large amount of unlabeled data. This can be particularly useful when labeling data is expensive or time-consuming. ### 2. Active Learning Active learning involves iter…
[2]Dec138b8 3361 428f B049 8ef1e4b6719e2 facts
ctx:claims/beam/dec138b8-3361-428f-b049-8ef1e4b6719e
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  labels = batch['labels'].to(device) outputs = model(input_ids, attention_mask=attention_mask, labels=labels) _, predicted = torch.max(outputs.scores, dim=1) total_correct += (predicted == lab…
[3]F266ef67 57dd 4b1f B9ab 661effb75c4b1 fact
ctx:claims/beam/f266ef67-57dd-4b1f-b9ab-661effb75c4b
[4]0e4dede6 52a5 49ce A450 4813d17383591 fact
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- full textbeam-chunk
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  - Load and split the dataset into training and testing sets. - Tokenize the data using the tokenizer. 2. **Model Fine-Tuning**: - Define a custom dataset class to handle the tokenized data. - Set up training arguments and defin…
[5]tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims5 facts
tp:paper:c75b96b4-5c8e-4a8f-bf4c-2af6ba7423d9:claims
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  nighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. Scaling laws for neural language models. arXiv [cs.LG], Jan. 2020. E. Mercado and S. Handel. Understanding the structure of humpback whale songs (l). The Jo…
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  Marine Science, 11:1394695, 2024. J. A. Allen, E. C. Garland, C. Garrigue, R. A. Dunlop, and M. J. Noad. Song complexity is maintained during inter-population cultural transmission of humpback whale songs. Scientific reports, 12(1): 8999, 2…
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  atasets with thousands of classes can be high performing, even on out-of-domain down- stream tasks. Next, the ‘bittern lesson’ learned when training Perch 2.0 was that bird species classification in particular is a challenging su- pervision…
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  = 8k = 16k = 8 k = 16k = 8 k = 16 GMWM0.8900.9140.7640.8210.9360.9540.868* 0.917*0.8230.855 SurfPerch 0.9320.9470.8590.9030.9810.9840.7960.8990.982* 0.986* Perch 1.0 0.9580.9680.9010.9310.9770.9810.8360.9050.9580.970 Perch 2.0 0.9…
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  V2.348 kHz3.0102420.0MBirds, Frogs AVES-bio16 kHzVariable768 2 94.4MGeneral Audio BirdAVES (large)16 kHzVariable1024 3 315.4MGeneral Audio + Birds 4 Comparison models. As our goal is to provide guidance on which pretrained embedding models …
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  ludes new classes unseen by the models. The classes used in the NOAA PIPAN evaluation set include anthropomorphic noise, unknown whale species, and the following baleen whale species: common minke whale, humpback whale, sei whale, blue whal…
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  ained on log-mel spectrograms using a classification loss. Additionally, the model used a form of self-distillation and a self-supervised loss (in the form of source recording prediction) with the goal of producing strong embeddings that ar…
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  ion as new sounds are discovered while not having large amounts of human labeled data. Despite these challenges, passive acoustic monitoring is a critical tool for marine conservation and ecology (Fleishman et al., 2023), and discoveries ab…
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  Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind Abs…
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  monitoring. Ecol. Inform., 61(101236):101236, Mar. 2021. 6 J. Kaplan, S. McCandlish, T. Henighan, T. B. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei. Scaling laws for neural language models. arXiv [cs.LG], Jan. 2020…
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  e datasets with thousands of classes can be high performing, even on out-of-domain down- stream tasks. Next, the ‘bittern lesson’ learned when training Perch 2.0 was that bird species classification in particular is a challenging su- pervis…
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  ce on which pretrained embedding models should be used for agile modeling and transfer learning (with existing tools), we limit our comparisons to models supported in the Perch Hoplite Github repository 5 . We compare the performance of the…
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  l of producing strong embeddings that are linearly separable for a wide range of bioacoustics tasks. Embeddings from the Perch model have shown successful generalization to tasks other than species classification (e.g., individual identific…
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  Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind Abs…
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  Perch 2.0 transfers ‘whale’ to underwater tasks Andrea Burns ∗ Google DeepMind Lauren Harrell ∗ Google Research Bart van Merriënboer Google DeepMind Vincent Dumoulin Google DeepMind Jenny Hamer Google DeepMind Tom Denton Google DeepMind A…

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Transfer Learning

Inbound mentions (7)

hasGoalHas Goal(2)

employsEmploys(1)

enableEnable(1)

hasHighPerformanceForHas High Performance for(1)

hasPartHas Part(1)

highPerformanceForHigh Performance for(1)

Other facts (17)

Timeline

References (5)

See also

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`hasGoal`Has Goal(2)

`employs`Employs(1)

`enable`Enable(1)

`hasHighPerformanceFor`Has High Performance for(1)

`hasPart`Has Part(1)

`highPerformanceFor`High Performance for(1)