Release notes v2.11

ReleaseNotes.md

@@ -1,5 +1,50 @@
 # Release Notes
 
+## New in Release 2.11.0
+
+Post-training Quantization:
+
+- Breaking changes:
+  - ...
+- General:
+  - ...
+- Features:
+  - (OpenVINO) Added Scale Estimation algorithm for 4-bit data-aware weights compression. This algorithm estimates more optimal scale in compression scheme based on difference between matrix multiplication in original precision and compressed one. For that `scale_estimation` optional parameter has been added to `nncf.compress_weights()` and can be used to minimize accuracy degradation of compressed models (note that this option increases the compression time).
+- Fixes:
+  - ...
+- Improvements:
+  - ...
+- Deprecations/Removals:
+  - Remove extra dependencies to install backends from setup.py (like `[torch]` are `[tf]`, `[onnx]` and `[openvino]`).
+- Tutorials:
+  - [Post-Training Optimization of Stable Video Diffusion Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-video-diffusion/stable-video-diffusion.ipynb)
+  - [Post-Training Optimization of YOLOv10 Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/yolov10-optimization/yolov10-optimization.ipynb)
+  - [Post-Training Optimization of LLaVA Next Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/nano-llava-multimodal-chatbot/nano-llava-multimodal-chatbot.ipynb)
+  - [Post-Training Optimization of S3D MIL-NCE Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/s3d-mil-nce-text-to-video-retrieval/s3d-mil-nce-text-to-video-retrieval.ipynb)
+  - [Post-Training Optimization of Stable Cascade Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/stable-cascade-image-generation/stable-cascade-image-generation.ipynb)
+- Known issues:
+  - ...
+
+Compression-aware training:
+
+- Breaking changes:
+  - ...
+- General:
+  - ...
+- Features:
+  - (PyTorch) `nncf.quantize` method now is the recommended path for the quantization initialization for Quantization-Aware Training.
+  - (PyTorch) Compression modules placement in the model now can be serialized and restored with new API functions: `compressed_model.nncf.get_config()` and `nncf.torch.load_from_config`. The [documentation](/docs/usage/training_time_compression/quantization_aware_training/Usage.md#saving-and-loading-compressed-models) and Resnet18 [example](examples/quantization_aware_training/torch/resnet18) was updated to use the new API.
+- Fixes:
+  - ...
+- Improvements:
+  - ...
+- Deprecations/Removals:
+  - ...
+- Tutorials:
+  - [Quantization-Sparsity Aware Training of PyTorch ResNet-50 Model](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks/pytorch-quantization-sparsity-aware-training/pytorch-quantization-sparsity-aware-training.ipynb)
+- Known issues:
+  - ...
+
 ## New in Release 2.10.0
 
 Post-training Quantization: