Row 7079

Row ID: 7079 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 7079 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

MRL \[1\] for CLIP allows smaller dimension embeddings to be used without loss in fidelity. Training is modified to optimize for truncated embeddings (multiple target dimensions at once) across both vision and text encoders.

Key findings:

* Reducing embeddings size by 4x retains \~95 performance * Projection layers for sub-embeddings did not help performance * Works in and out (zero-shot) of domain on multi-modal retrieval * Using too many sub-embeddings degrades performance (i.e. {512, 256, 128} vs {512, 256, 128, 64, 32, 16, 8} * The number of sub-embeddings impacts convergence (same as above) * Works with rank-tuning methods like GCL * Relative importance (weighting, wi) of sub-dimensions matters (e.g. w1\*L\_512 + w2\*L\_256 + w3\*L\_128) * MRL trained models can improve if the original sized embedding is used. i.e. performance is improved even if the smaller embeddings are not used.

Article: [https://www.marqo.ai/blog/matryoshka-representation-learning-with-clip-for-multimodal-retrieval-and-ranking](https://www.marqo.ai/blog/matryoshka-representation-learning-with-clip-for-multimodal-retrieval-and-ranking)

\[1\] MRL [https://arxiv.org/abs/2205.13147](https://arxiv.org/abs/2205.13147)

FieldValue
text MRL \[1\] for CLIP allows smaller dimension embeddings to be used without loss in fidelity. Training is modified to optimize for truncated embeddings (multiple target dimensions at once) across both vision and text encoders. Key findings: * Reducing embeddings size by 4x retains \~95 performance * Projection layers for sub-embeddings did not help performance * Works in and out (zero-shot) of domain on multi-modal retrieval * Using too many sub-embeddings degrades performance (i.e. {512, 2…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-15
username_encoded Z0FBQUFBQm5LakwzT2JYMC10SEZyU2o3a2lEQWRTTU00bmJjcHg5R2ZUYmRXTF9jVDgxTU9Dblptclg1eE9PcllWaDAxMERJdk00bEtFaU9oVUFXSTRMUDlSOUJyWmVTenc9PQ==
url_encoded Z0FBQUFBQm5Lak9HLXMwNkN3LWJiM2tPeU8yTDlsOFJaUXhJNkt5bWFraENHbFc1ekRISEFPSG4ybkxRbkJic3V4M05yMlZpS3UtZEcyeXF5TjdCTzhfVVg0ejQ3N3Q4Q0dCZUViZjJWbHloVzg4allBS2VFdW8tYk5pYm9oUXF3MXZfLW9nd2daUC1nSVQ2cExsSWRZcWNkOGNBMk42ek1EbXcyOEl4WFoyZ3E4aUhYZGNLbnVBWmk1ZHdzRlR4U1l0WGE4bzY0NzE3NnpGejlveFZTYm9NVUlITnN4dFI1dz09

Raw Record

{
  "text": "MRL \\[1\\] for CLIP allows smaller dimension embeddings to be used without loss in fidelity. Training is modified to optimize for truncated embeddings (multiple target dimensions at once) across both vision and text encoders.  \n\n  \nKey findings:\n\n* Reducing embeddings size by 4x retains \\~95 performance\n* Projection layers for sub-embeddings did not help performance\n* Works in and out (zero-shot) of domain on multi-modal retrieval\n* Using too many sub-embeddings degrades performance (i.e. {512, 256, 128} vs {512, 256, 128, 64, 32, 16, 8}\n* The number of sub-embeddings impacts convergence (same as above)\n* Works with rank-tuning methods like GCL\n* Relative importance (weighting, wi) of sub-dimensions matters (e.g. w1\\*L\\_512 + w2\\*L\\_256 + w3\\*L\\_128)\n* MRL trained models can improve if the original sized embedding is used. i.e. performance is improved even if the smaller embeddings are not used.\n\nArticle:  \n[https://www.marqo.ai/blog/matryoshka-representation-learning-with-clip-for-multimodal-retrieval-and-ranking](https://www.marqo.ai/blog/matryoshka-representation-learning-with-clip-for-multimodal-retrieval-and-ranking)\n\n\\[1\\] MRL [https://arxiv.org/abs/2205.13147](https://arxiv.org/abs/2205.13147)",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-15",
  "username_encoded": "Z0FBQUFBQm5LakwzT2JYMC10SEZyU2o3a2lEQWRTTU00bmJjcHg5R2ZUYmRXTF9jVDgxTU9Dblptclg1eE9PcllWaDAxMERJdk00bEtFaU9oVUFXSTRMUDlSOUJyWmVTenc9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9HLXMwNkN3LWJiM2tPeU8yTDlsOFJaUXhJNkt5bWFraENHbFc1ekRISEFPSG4ybkxRbkJic3V4M05yMlZpS3UtZEcyeXF5TjdCTzhfVVg0ejQ3N3Q4Q0dCZUViZjJWbHloVzg4allBS2VFdW8tYk5pYm9oUXF3MXZfLW9nd2daUC1nSVQ2cExsSWRZcWNkOGNBMk42ek1EbXcyOEl4WFoyZ3E4aUhYZGNLbnVBWmk1ZHdzRlR4U1l0WGE4bzY0NzE3NnpGejlveFZTYm9NVUlITnN4dFI1dz09"
}
Explore Dataset Explore Row

Entry Information